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Massimiliano Marcellino

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.

    Mentioned in:

    1. > Econometrics > Forecasting > Nowcasting
    2. > Econometrics > Time Series Models > VAR Models > Time Varying Parameters and Stochastic Volatility
  2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.

    Mentioned in:

    1. > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)
  3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.

    Mentioned in:

    1. > Econometrics > Forecasting
  4. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.

    Mentioned in:

    1. > Econometrics > Time Series Models > Dynamic Factor Models > Structural Factor Models

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Òscar Jordà & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.

    Mentioned in:

    1. Path forecast evaluation (Journal of Applied Econometrics 2010) in ReplicationWiki ()
  2. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.

    Mentioned in:

    1. Principal components at work: the empirical analysis of monetary policy with large data sets (Journal of Applied Econometrics 2005) in ReplicationWiki ()
  3. Massimiliano Marcellino & Grayham E. Mizon, 2001. "Small-system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 359-370.

    Mentioned in:

    1. Small-system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994 (Journal of Applied Econometrics 2001) in ReplicationWiki ()
  4. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.

    Mentioned in:

    1. Forecasting large datasets with Bayesian reduced rank multivariate models (Journal of Applied Econometrics 2011) in ReplicationWiki ()

Working papers

  1. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2024. "Specification Choices in Quantile Regression for Empirical Macroeconomics," CEPR Discussion Papers 18901, C.E.P.R. Discussion Papers.

    Cited by:

    1. Jan Prüser & Florian Huber, 2024. "Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 269-291, March.

  2. Niko Hauzenberger & Massimiliano Marcellino & Michael Pfarrhofer & Anna Stelzer, 2024. "Nowcasting with Mixed Frequency Data Using Gaussian Processes," Papers 2402.10574, arXiv.org, revised Sep 2024.

    Cited by:

    1. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.

  3. Andrea Carriero & Massimiliano Marcellino & Tommaso Tornese, 2023. "Blended Identification in Structural VARs," BAFFI CAREFIN Working Papers 23200, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Jan Pruser, 2024. "A large non-Gaussian structural VAR with application to Monetary Policy," Papers 2412.17598, arXiv.org.

  4. Massimiliano Marcellino & Dalibor Stevanovic, 2022. "The demand and supply of information about inflation," Working Papers 22-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2022.

    Cited by:

    1. David Ardia & Keven Bluteau, 2024. "Optimal Text-Based Time-Series Indices," Papers 2405.10449, arXiv.org.

  5. Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.

    Cited by:

    1. Florian Huber & Josef Schreiner, 2023. "Are Phillips curves in CESEE still alive and well behaved?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q3/23, pages 7-27.
    2. Joshua C. C. Chan, 2024. "BVARs and stochastic volatility," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 3, pages 43-67, Edward Elgar Publishing.
    3. Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Predictive Density Combination Using a Tree-Based Synthesis Function," Working Papers 23-30, Federal Reserve Bank of Cleveland.
    4. Michael Pfarrhofer, 2024. "Forecasts with Bayesian vector autoregressions under real time conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 771-801, April.
    5. Oyebayo Ridwan Olaniran & Ali Rashash R. Alzahrani, 2023. "On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression," Mathematics, MDPI, vol. 11(24), pages 1-29, December.
    6. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    7. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    8. Marcellino, Massimiliano & Pfarrhofer, Michael, 2024. "Bayesian nonparametric methods for macroeconomic forecasting," CEPR Discussion Papers 18970, C.E.P.R. Discussion Papers.
    9. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Forecasting euro area inflation with machine-learning models," Research Bulletin, European Central Bank, vol. 112.
    10. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    11. Jan Prüser & Florian Huber, 2024. "Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 269-291, March.
    12. Tibor Szendrei & Arnab Bhattacharjee, 2024. "Momentum Informed Inflation-at-Risk," Papers 2408.12286, arXiv.org.
    13. Florian Huber & Karin Klieber & Massimiliano Marcellino & Luca Onorante & Michael Pfarrhofer, 2024. "Asymmetries in Financial Spillovers," Papers 2410.16214, arXiv.org.
    14. Sui, Jianli & Lv, Wenqiang & Gao, Xiang & Koedijk, Kees G., 2024. "China’s GDP-at-Risk: Real-Time Monitoring, Risk Tracing, and Macroeconomic Policy Effects," Journal of International Money and Finance, Elsevier, vol. 147(C).
    15. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    16. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    17. Huang, Yu-Fan & Liao, Wenting & Wang, Taining, 2024. "Does US financial uncertainty spill over through the (asymmetric) international credit channel? The role of market expectations," Journal of International Money and Finance, Elsevier, vol. 148(C).
    18. Martin Feldkircher & Luis Gruber & Florian Huber & Gregor Kastner, 2024. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2126-2145, September.

  6. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.

    Cited by:

    1. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
    2. Martin Gachter & Elias Hasler & Florian Huber, 2023. "A tale of two tails: 130 years of growth-at-risk," Papers 2302.08920, arXiv.org.
    3. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    4. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    5. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    6. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    7. Jan Prüser & Florian Huber, 2024. "Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 269-291, March.
    8. Jacobi Liana & Kwok Chun Fung & Ramírez-Hassan Andrés & Nghiem Nhung, 2024. "Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 403-434, April.

  7. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.

    Cited by:

    1. Szendrei, Tibor & Varga, Katalin, 2023. "Revisiting vulnerable growth in the Euro Area: Identifying the role of financial conditions in the distribution," Economics Letters, Elsevier, vol. 223(C).
    2. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    3. Hilde C. Bjørnland & Roberto Casarin & Marco Lorusso & Francesco Ravazzolo, 2023. "Fiscal Policy Regimes in Resource-Rich Economies," Working Papers No 13/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    5. Falconio, Andrea & Manganelli, Simone, 2020. "Financial conditions, business cycle fluctuations and growth at risk," Working Paper Series 2470, European Central Bank.
    6. Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2024. "Labour at risk," European Economic Review, Elsevier, vol. 170(C).
    7. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    8. Martin Iseringhausen, 2021. "A time-varying skewness model for Growth-at-Risk," Working Papers 49, European Stability Mechanism.
    9. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.
    10. Martin Gachter & Elias Hasler & Florian Huber, 2023. "A tale of two tails: 130 years of growth-at-risk," Papers 2302.08920, arXiv.org.
    11. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    12. Patrick A. Adams & Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2020. "Forecasting Macroeconomic Risks," Staff Reports 914, Federal Reserve Bank of New York.
    13. Anthony Garratt & Timo Henckel & Shaun P. Vahey, 2019. "Empirically-transformed linear opinion pools," CAMA Working Papers 2019-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Schick, Manuel, 2024. "Real-time Nowcasting Growth-at-Risk using the Survey of Professional Forecasters," Working Papers 0750, University of Heidelberg, Department of Economics.
    15. Kevin Moran & Dalibor Stevanovic & Stéphane Surprenant, 2024. "Risk Scenarios and Macroeconomic Impacts: Insights for Canadian Policy," CIRANO Working Papers 2024s-03, CIRANO.
    16. Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2022. "The Relation between the High-Yield Bond Spread and the Unemployment Rate in the Euro Area," Finance Research Letters, Elsevier, vol. 46(PA).
    17. Deng, Chuang & Wu, Jian, 2023. "Macroeconomic downside risk and the effect of monetary policy," Finance Research Letters, Elsevier, vol. 54(C).
    18. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    19. Mihail Yanchev, 2022. "Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 20-41.
    20. Leopoldo Catania & Alessandra Luati & Pierluigi Vallarino, 2021. "Economic vulnerability is state dependent," CREATES Research Papers 2021-09, Department of Economics and Business Economics, Aarhus University.
    21. Sui, Jianli & Lv, Wenqiang & Gao, Xiang & Koedijk, Kees G., 2024. "China’s GDP-at-Risk: Real-Time Monitoring, Risk Tracing, and Macroeconomic Policy Effects," Journal of International Money and Finance, Elsevier, vol. 147(C).
    22. Huang, Yu-Fan & Liao, Wenting & Luo, Sui & Ma, Jun, 2024. "Financial conditions, macroeconomic uncertainty, and macroeconomic tail risks," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    23. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2024.
    24. Yannick Hoga & Christian Schulz, 2025. "Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series," Papers 2502.10065, arXiv.org.

  8. Bai, Yu & Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022. "Macroeconomic Forecasting in a Multi-country Context," CEPR Discussion Papers 16994, C.E.P.R. Discussion Papers.

    Cited by:

    1. Florian Huber & Gary Koop & Massimiliano Marcellino & Tobias Scheckel, 2024. "Bayesian modelling of VAR precision matrices using stochastic block networks," Papers 2407.16349, arXiv.org.
    2. Florian Huber & Karin Klieber & Massimiliano Marcellino & Luca Onorante & Michael Pfarrhofer, 2024. "Asymmetries in Financial Spillovers," Papers 2410.16214, arXiv.org.
    3. Hauzenberger , Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2024. "Bayesian Neural Networks for Macroeconomic Analysis," CEPR Discussion Papers 19381, C.E.P.R. Discussion Papers.

  9. Foroni, Claudia & Gelain, Paolo & Marcellino, Massimiliano, 2022. "The financial accelerator mechanism: does frequency matter?," Working Paper Series 2637, European Central Bank.

    Cited by:

    1. Gelfer, Sacha & Gibbs, Christopher G., 2023. "Measuring the effects of large-scale asset purchases: The role of international financial markets and the financial accelerator," Journal of International Money and Finance, Elsevier, vol. 131(C).
    2. Guido Ascari & Qazi Haque & Leandro M. Magnusson & Sophocles Mavroeidis, 2021. "Empirical evidence on the Euler equation for investment in the US," CAMA Working Papers 2021-65, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

  10. Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022. "Bayesian Neural Networks for Macroeconomic Analysis," Papers 2211.04752, arXiv.org, revised Apr 2024.

    Cited by:

    1. Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2024. "Machine Learning the Macroeconomic Effects of Financial Shocks," Papers 2412.07649, arXiv.org.

  11. Andrea Carriero & Massimiliano Marcellino & Tommaso Tornese, 2022. "Macro Uncertainty in the Long Run," BAFFI CAREFIN Working Papers 22188, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Mathias Krogh & Giovanni Pellegrino, "undated". "Real Activity and Uncertainty Shocks: The Long and the Short of It," "Marco Fanno" Working Papers 0310, Dipartimento di Scienze Economiche "Marco Fanno".
    2. Emanuele Bacchiocchi & Catalin Dragomirescu-Gaina, 2022. "Uncertainty spill-overs: when policy and financial realms overlap," Working Papers wp1174, Dipartimento Scienze Economiche, Universita' di Bologna.

  12. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.

    Cited by:

    1. Eraslan, Sercan & Reif, Magnus, 2023. "A latent weekly GDP indicator for Germany," Technical Papers 08/2023, Deutsche Bundesbank.
    2. Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2022. ""Daily Growth at Risk: financial or real drivers? The answer is not always the same"," IREA Working Papers 202208, University of Barcelona, Research Institute of Applied Economics, revised Jun 2022.
    3. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
    4. Antolín-Díaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2024. "Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails," Journal of Econometrics, Elsevier, vol. 238(2).
    5. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2023. "Expecting the unexpected: Stressed scenarios for economic growth," Working Papers 202314, University of California at Riverside, Department of Economics.
    6. Ignacio Garr'on & Andrey Ramos, 2025. "High-frequency Density Nowcasts of U.S. State-Level Carbon Dioxide Emissions," Papers 2501.03380, arXiv.org.
    7. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2022. "Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP," Papers 2209.01910, arXiv.org.
    8. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    9. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    10. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2022. "Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications," Papers 2211.16121, arXiv.org, revised Aug 2024.
    11. Schick, Manuel, 2024. "Real-time Nowcasting Growth-at-Risk using the Survey of Professional Forecasters," Working Papers 0750, University of Heidelberg, Department of Economics.
    12. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.
    13. Narasingha Das & Partha Gangopadhyay, 2023. "Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.

  13. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Schorfheide, Frank & Aruoba, Boragan & Mlikota, Marko & Villalvazo, Sergio, 2021. "SVARs With Occasionally-Binding Constraints," CEPR Discussion Papers 15923, C.E.P.R. Discussion Papers.

  14. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2021. "Using Time-Varying Volatility for Identification in Vector Autoregressions: An Application to Endogenous Uncertainty," CEPR Discussion Papers 16346, C.E.P.R. Discussion Papers.

    Cited by:

    1. Chang, Hao-Wen & Chang, Tsangyao & Lee, Chien-Chiang, 2023. "Return and volatility connectedness among the BRICS stock and oil markets," Resources Policy, Elsevier, vol. 86(PA).
    2. Carlos Moreno Pérez & Marco Minozzo, 2022. "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers 2240, Banco de España.
    3. Emanuele Bacchiocchi & Catalin Dragomirescu-Gaina, 2022. "Uncertainty spill-overs: when policy and financial realms overlap," Working Papers wp1174, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Olli Palm'en, 2022. "Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian VAR approach," Papers 2202.10834, arXiv.org.
    5. Gnangnon, Sèna Kimm, 2023. "Effect of Economic Uncertainty on Remittances Flows from Developed Countries," EconStor Preprints 279480, ZBW - Leibniz Information Centre for Economics.
    6. Che, Ming & Zhu, Zixiang & Li, Yujia, 2023. "Geopolitical risk and economic policy uncertainty: Different roles in China's financial cycle," International Review of Financial Analysis, Elsevier, vol. 90(C).
    7. Bobasu, Alina & Geis, André & Quaglietti, Lucia & Ricci, Martino, 2021. "Tracking global economic uncertainty: implications for the euro area," Working Paper Series 2541, European Central Bank.
    8. Andrea Carriero & Alessio Volpicella, 2022. "Generalizing the Max Share Identification to multiple shocks identification: an Application to Uncertainty," School of Economics Discussion Papers 0322, School of Economics, University of Surrey.
    9. Andrea Carriero & Massimiliano Marcellino & Tommaso Tornese, 2022. "Macro Uncertainty in the Long Run," BAFFI CAREFIN Working Papers 22188, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    10. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    11. Ambrocio, Gene, 2020. "Inflationary household uncertainty shocks," Bank of Finland Research Discussion Papers 5/2020, Bank of Finland.
    12. Beckmann, Joscha & Czudaj, Robert L., 2024. "Uncertainty Shocks and Inflation: The Role of Credibility and Expectation Anchoring," MPRA Paper 119971, University Library of Munich, Germany.
    13. Yujia, Li & Zixiang, Zhu & Ming, Che, 2024. "Exploring the relationship between China's economic policy uncertainty and business cycles: Exogenous impulse or endogenous responses?," Emerging Markets Review, Elsevier, vol. 58(C).
    14. Gabriele Fiorentini & Alessio Moneta & Francesca Papagni, 2024. "Identification of one independent shock in structural VARs," LEM Papers Series 2024/28, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    15. Koivisto, Tero, 2024. "Asset price shocks and inflation in the Finnish economy," BoF Economics Review 6/2024, Bank of Finland.
    16. Andreas Dibiasi & Samad Sarferaz, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," Post-Print hal-04167343, HAL.
    17. Sèna Kimm Gnangnon, 2024. "The effect of economic uncertainty on remittance flows from developed countries," Economic Affairs, Wiley Blackwell, vol. 44(2), pages 267-280, June.

  15. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd & Mertens, Elmar, 2021. "Measuring Uncertainty and Its Effects in the COVID-19 Era," CEPR Discussion Papers 15965, C.E.P.R. Discussion Papers.

    Cited by:

    1. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    2. Luis J. Álvarez & Florens Odendahl, 2022. "Data outliers and Bayesian VARs in the Euro Area," Working Papers 2239, Banco de España.
    3. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).

  16. Massimo Guidolin & Davide La Cara & Massimiliano Marcellino, 2021. "Boosting the Forecasting Power of Conditional Heteroskedasticity Models to Account for Covid-19 Outbreaks," BAFFI CAREFIN Working Papers 21169, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    2. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    3. Beatrice Franzolini & Alexandros Beskos & Maria De Iorio & Warrick Poklewski Koziell & Karolina Grzeszkiewicz, 2022. "Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market," Papers 2208.00952, arXiv.org, revised May 2023.

  17. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Papers 2110.03411, arXiv.org.

    Cited by:

    1. Dimitris Korobilis & Maximilian Schröder, 2023. "Probabilistic Quantile Factor Analysis," Working Papers No 05/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Simon Lloyd & Ed Manuel & Konstantin Panchev, 2024. "Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(1), pages 335-392, March.
    3. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Papers No 06/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    5. Ignace De Vos & Gerdie Everaert, 2024. "GLS Estimation of Local Projections: Trading Robustness for Efficiency," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 24/1095, Ghent University, Faculty of Economics and Business Administration.
    6. Lv, Mengdi & Jiao, Shoukun & Ye, Shiqi & Song, Hongmei & Xu, Jiexin & Ye, Wuyi, 2024. "Assessing time-varying risk in China’s GDP growth," Economics Letters, Elsevier, vol. 242(C).
    7. Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2024. "Machine Learning the Macroeconomic Effects of Financial Shocks," Papers 2412.07649, arXiv.org.
    8. Vegard Høghaug Larsen & Nicolò Maffei-Faccioli & Laura Pagenhardt, 2023. "Where do they care? The ECB in the media and inflation expectations," Working Papers No 04/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

  18. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea & Mertens, Elmar, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," CEPR Discussion Papers 15964, C.E.P.R. Discussion Papers.

    Cited by:

    1. Barend Abeln & Jan P.A.M. Jacobs, 2021. "COVID-19 and seasonal adjustment," CAMA Working Papers 2021-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    3. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    4. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    5. Serena Ng, 2021. "Modeling Macroeconomic Variations After COVID-19," Papers 2103.02732, arXiv.org, revised Jul 2021.
    6. Nalban, Valeriu & Smădu, Andra, 2021. "Asymmetric effects of uncertainty shocks: Normal times and financial disruptions are different," Journal of Macroeconomics, Elsevier, vol. 69(C).
    7. Antolín-Díaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2024. "Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails," Journal of Econometrics, Elsevier, vol. 238(2).
    8. Florian Huber, 2023. "Bayesian Nonlinear Regression using Sums of Simple Functions," Papers 2312.01881, arXiv.org.
    9. Daniele Valenti & Andrea Bastianin & Matteo Manera, 2022. "A weekly structural VAR model of the US crude oil market," Working Papers 2022.11, Fondazione Eni Enrico Mattei.
    10. Camilo Granados & Daniel Parra-Amado, 2023. "Estimating the Output Gap After COVID: How to Address Unprecedented Macroeconomic Variations," Borradores de Economia 1249, Banco de la Republica de Colombia.
    11. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    12. Davidson, Sharada Nia & Moccero, Diego Nicolas, 2024. "The nonlinear effects of banks’ vulnerability to capital depletion in euro area countries," Working Paper Series 2912, European Central Bank.
    13. Morão, Hugo, 2024. "An economic policy uncertainty index for Portugal," International Economics, Elsevier, vol. 178(C).
    14. Vito Polito & Yunyi Zhang, 2021. "Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression," CESifo Working Paper Series 9395, CESifo.
    15. Evgenidis, Anastasios & Fasianos, Apostolos, 2023. "Modelling monetary policy’s impact on labour markets under Covid-19," Economics Letters, Elsevier, vol. 230(C).
    16. Frank Schorfheide & Dongho Song, 2024. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 275-320, October.
    17. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    18. Arabinda Basistha, "undated". "Estimates of Quarterly and Monthly Episodes of Global Recessions: Evidence from Markov-switching Dynamic Factor Models," Working Papers 24-07, Department of Economics, West Virginia University.
    19. Hwee Kwan Chow & Keen Meng Choy, 2023. "Economic forecasting in a pandemic: some evidence from Singapore," Empirical Economics, Springer, vol. 64(5), pages 2105-2124, May.
    20. Prüser, Jan, 2021. "The horseshoe prior for time-varying parameter VARs and Monetary Policy," Journal of Economic Dynamics and Control, Elsevier, vol. 129(C).
    21. Colunga-Ramos, Luis Fernando & Cepeda, Leonardo E. Torre, 2024. "Regional supply, demand and labor shocks on the manufacturing sector during COVID-19 in Mexico," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(2).
    22. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    23. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
    24. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    25. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    26. Barauskaitė, Kristina & Nguyen, Anh D.M. & Rousová, Linda & Cappiello, Lorenzo, 2022. "The impact of credit supply shocks in the euro area: market-based financing versus loans," Working Paper Series 2673, European Central Bank.
    27. José R. Maria & Paulo Júlio, 2023. "Trends and cycles during the COVID-19 pandemic period," Working Papers w202311, Banco de Portugal, Economics and Research Department.
    28. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    29. Budnik, Katarzyna & Groß, Johannes & Vagliano, Gianluca & Dimitrov, Ivan & Lampe, Max & Panos, Jiri & Velasco, Sofia & Boucherie, Louis & Jančoková, Martina, 2023. "BEAST: A model for the assessment of system-wide risks and macroprudential policies," Working Paper Series 2855, European Central Bank.
    30. Sun, Weihong & Liu, Ding, 2023. "Great moderation with Chinese characteristics: Uncovering the role of monetary policy," Economic Modelling, Elsevier, vol. 121(C).
    31. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
    32. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.
    33. Luis J. Álvarez & Florens Odendahl, 2022. "Data outliers and Bayesian VARs in the Euro Area," Working Papers 2239, Banco de España.
    34. Colunga L. Fernando & Torre Cepeda Leonardo, 2023. "Effects of Supply, Demand, and Labor Market Shocks in the Mexican Manufacturing Sector," Working Papers 2023-10, Banco de México.
    35. Budnik, Katarzyna & Ponte Marques, Aurea & Giglio, Carla & Grassi, Alberto & Durrani, Agha & Figueres, Juan Manuel & Konietschke, Paul & Le Grand, Catherine & Metzler, Julian & Población García, Franc, 2024. "Advancements in stress-testing methodologies for financial stability applications," Occasional Paper Series 348, European Central Bank.
    36. Kiss, Tamas & Nguyen, Hoang & Österholm, Pär, 2022. "Modelling Okun’s Law – Does non-Gaussianity Matter?," Working Papers 2022:1, Örebro University, School of Business.
    37. Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    38. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    39. Martin Iseringhausen & Konstantinos Theodoridis, 2025. "A survey-based measure of asymmetric macroeconomic risk in the euro area," Working Papers 68, European Stability Mechanism, revised 11 Feb 2025.
    40. Bańbura, Marta & Belousova, Irina & Bodnár, Katalin & Tóth, Máté Barnabás, 2023. "Nowcasting employment in the euro area," Working Paper Series 2815, European Central Bank.
    41. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.
    42. Andrejs Zlobins, 2021. "On the Time-varying Effects of the ECB's Asset Purchases," Working Papers 2021/02, Latvijas Banka.
    43. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    44. Danilo Cascaldi-Garcia, 2022. "Pandemic Priors," International Finance Discussion Papers 1352, Board of Governors of the Federal Reserve System (U.S.).
    45. Allayioti, Anastasia & Gόrnicka, Lucyna & Holton, Sarah & Martínez Hernández, Catalina, 2024. "Monetary policy pass-through to consumer prices: evidence from granular price data," Working Paper Series 3003, European Central Bank.
    46. Alanya-Beltran, Willy, 2022. "Unit roots in lower-bounded series with outliers," Economic Modelling, Elsevier, vol. 115(C).
    47. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    48. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  19. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2021. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Papers 2112.01995, arXiv.org, revised Nov 2022.

    Cited by:

    1. Andrea Renzetti, 2023. "Theory coherent shrinkage of Time-Varying Parameters in VARs," Papers 2311.11858, arXiv.org, revised Nov 2024.

  20. Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Papers 2103.01201, arXiv.org.

    Cited by:

    1. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    2. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
    4. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
    5. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    6. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    7. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    8. Ying Lun Cheung, 2024. "Identification of Time-Varying Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 76-94, January.
    9. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    10. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    11. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Working Papers 21-02, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    12. Niko Hauzenberger & Florian Huber & Karin Klieber, 2020. "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers 2012.08155, arXiv.org, revised Dec 2021.
    13. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    14. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.

  21. Marcellino, Massimiliano & Kapetanios, George & Dendramis, Yiannis, 2020. "A Similarity-based Approach for Macroeconomic Forecasting," CEPR Discussion Papers 14469, C.E.P.R. Discussion Papers.

    Cited by:

    1. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    2. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    3. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    4. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    5. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    6. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).

  22. Marcellino, Massimiliano & Kapetanios, George & Giraitis, Liudas, 2020. "Time-Varying Instrumental Variable Estimation," CEPR Discussion Papers 15210, C.E.P.R. Discussion Papers.

    Cited by:

    1. Michele Fratianni & Federico Giri & Riccardo Lucchetti & Francesco Valentini, 2022. "Monetization, wars, and the Italian fiscal multiplier," Mo.Fi.R. Working Papers 176, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    2. Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    3. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    4. Mikihito Nishi, 2024. "Estimating Time-Varying Parameters of Various Smoothness in Linear Models via Kernel Regression," Papers 2406.14046, arXiv.org, revised Mar 2025.
    5. Francisco Blasques & Noah Stegehuis, 2024. "A Score-Driven Filter for Causal Regression Models with Time- Varying Parameters and Endogenous Regressors," Tinbergen Institute Discussion Papers 24-016/III, Tinbergen Institute.
    6. Yu Bai & Massimiliano Marcellino & George Kapetanios, 2023. "Mean Group Instrumental Variable Estimation of Time-Varying Large Heterogeneous Panels with Endogenous Regressors," Monash Econometrics and Business Statistics Working Papers 13/23, Monash University, Department of Econometrics and Business Statistics.
    7. Riccardo “Jack” Lucchetti & Francesco Valentini, 2024. "Linear models with time-varying parameters: a comparison of different approaches," Computational Statistics, Springer, vol. 39(7), pages 3523-3545, December.

  23. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.

    Cited by:

    1. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    2. Hager Ben Romdhane, 2021. "Nowcasting in Tunisia using large datasets and mixed frequency models," IHEID Working Papers 11-2021, Economics Section, The Graduate Institute of International Studies.
    3. Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2024. "Labour at risk," European Economic Review, Elsevier, vol. 170(C).
    4. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    5. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    6. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    7. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    8. Berger, Tino & Morley, James & Wong, Benjamin, 2023. "Nowcasting the output gap," Journal of Econometrics, Elsevier, vol. 232(1), pages 18-34.
      • Tino Berger & James Morley & Benjamin Wong, 2020. "Nowcasting the output gap," CAMA Working Papers 2020-78, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Martin Iseringhausen, 2021. "A time-varying skewness model for Growth-at-Risk," Working Papers 49, European Stability Mechanism.
    10. Jean-Guillaume Sahuc & Matteo Mogliani & Laurent Ferrara, 2022. "High-frequency monitoring of growth at risk," Post-Print hal-03361425, HAL.
    11. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    12. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    13. Frank Schorfheide & Dongho Song, 2024. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 275-320, October.
    14. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    15. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2021. "Expecting the unexpected: economic growth under stress," Working Papers 202106, University of California at Riverside, Department of Economics.
    16. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Vo, Xuan Vinh, 2023. "Oil tail risks and the realized variance of consumer prices in advanced economies," Resources Policy, Elsevier, vol. 83(C).
    17. Teng, Bin & Wang, Sicong & Shi, Yufeng & Sun, Yunchuan & Wang, Wei & Hu, Wentao & Shi, Chaojun, 2022. "Economic recovery forecasts under impacts of COVID-19," Economic Modelling, Elsevier, vol. 110(C).
    18. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.
    19. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas, 2023. "Nowcasting Economic Activity Using Electricity Market Data: The Case of Lithuania," Economies, MDPI, vol. 11(5), pages 1-21, May.
    20. Jennifer Betz & Maximilian Nagl & Daniel Rösch, 2022. "Credit line exposure at default modelling using Bayesian mixed effect quantile regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2035-2072, October.
    21. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    22. Tibor Szendrei & Arnab Bhattacharjee & Mark E. Schaffer, 2024. "MIDAS-QR with 2-Dimensional Structure," Papers 2406.15157, arXiv.org.
    23. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.
    24. Forni, Mario & Gambetti, Luca & Maffei-Faccioli, Nicolò & Sala, Luca, 2024. "The effects of monetary policy on macroeconomic risk," European Economic Review, Elsevier, vol. 167(C).
    25. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
    26. Yfanti, Stavroula & Karanasos, Menelaos & Zopounidis, Constantin & Christopoulos, Apostolos, 2023. "Corporate credit risk counter-cyclical interdependence: A systematic analysis of cross-border and cross-sector correlation dynamics," European Journal of Operational Research, Elsevier, vol. 304(2), pages 813-831.

  24. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    2. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2018. "The global component of inflation volatility," Temi di discussione (Economic working papers) 1170, Bank of Italy, Economic Research and International Relations Area.

  25. Claudia Foroni & Massimiliano Marcellino & Dalibor Stevanovic, 2020. "Forecasting the COVID-19 recession and recovery: Lessons from the financial crisis," Working Papers 20-14, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2020.

    Cited by:

    1. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    2. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    4. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    5. Richard B. Freeman, 2022. "Planning for the “Expected Unexpected”: Work and Retirement in the U.S. After the COVID-19 Pandemic Shock," NBER Working Papers 29653, National Bureau of Economic Research, Inc.
    6. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    7. Lorenzo Fratoni & Susanna Levantesi & Massimiliano Menzietti, 2022. "Measuring Financial Sustainability and Social Adequacy of the Italian NDC Pension System under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    8. Serena Ng, 2021. "Modeling Macroeconomic Variations After COVID-19," Papers 2103.02732, arXiv.org, revised Jul 2021.
    9. Yose Rizal Damuri & Prabaning Tyas & Haryo Aswicahyono & Lionel Priyadi & Stella Kusumawardhani & Ega Kurnia Yazid, 2021. "Tracking the Ups and Downs in Indonesia’s Economic Activity During COVID-19 Using Mobility Index: Evidence from Provinces in Java and Bali," Working Papers DP-2021-18, Economic Research Institute for ASEAN and East Asia (ERIA).
    10. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
    11. Orkideh Gharehgozli & Sunhyung Lee, 2022. "Money Supply and Inflation after COVID-19," Economies, MDPI, vol. 10(5), pages 1-14, April.
    12. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    13. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
    14. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    15. Kevin Moran & Dalibor Stevanovic & Adam Abdel Kader Touré, 2020. "Macroeconomic Uncertainty and the COVID-19 Pandemic: Measure and Impacts on the Canadian Economy," CIRANO Working Papers 2020s-47, CIRANO.
    16. Frank Schorfheide & Dongho Song, 2024. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 275-320, October.
    17. De Backer, Bruno & Dewachter, Hans & Iania, Leonardo, 2021. "Macrofinancial information on the post- COVID-19 economic recovery: will it be V, U or L-shaped?," LIDAM Discussion Papers LFIN 2021002, Université catholique de Louvain, Louvain Finance (LFIN).
    18. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    19. Kevin Moran & Dalibor Stevanovic & Stéphane Surprenant, 2024. "Risk Scenarios and Macroeconomic Impacts: Insights for Canadian Policy," CIRANO Working Papers 2024s-03, CIRANO.
    20. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    21. John O’Trakoun, 2022. "Business forecasting during the pandemic," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 57(3), pages 95-110, July.
    22. Yannis Psycharis & Anastasia Panori & Dimitrios Athanasopoulos, 2022. "Public Investment and Regional Resilience: Empirical Evidence from the Greek Regions," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 113(1), pages 57-79, February.
    23. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    24. Cassetti, Gabriele & Boitier, Baptiste & Elia, Alessia & Le Mouël, Pierre & Gargiulo, Maurizio & Zagamé, Paul & Nikas, Alexandros & Koasidis, Konstantinos & Doukas, Haris & Chiodi, Alessandro, 2023. "The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: An energy-economic modelling perspective," Energy, Elsevier, vol. 263(PC).
    25. Zhao, Xinyue & Chen, Heng & Zheng, Qiwei & Liu, Jun & Pan, Peiyuan & Xu, Gang & Zhao, Qinxin & Jiang, Xue, 2023. "Thermo-economic analysis of a novel hydrogen production system using medical waste and biogas with zero carbon emission," Energy, Elsevier, vol. 265(C).
    26. Aminullah, Erman, 2024. "Forecasting of technology innovation and economic growth in Indonesia," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    27. Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    28. Lu, Fei & Zeng, Qing & Bouri, Elie & Tao, Ying, 2024. "Forecasting US GDP growth rates in a rich environment of macroeconomic data," International Review of Economics & Finance, Elsevier, vol. 95(C).
    29. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    30. Nadiia Shapovalenko, 2021. "A BVAR Model for Forecasting Ukrainian Inflation," IHEID Working Papers 05-2021, Economics Section, The Graduate Institute of International Studies.
    31. Teng, Bin & Wang, Sicong & Shi, Yufeng & Sun, Yunchuan & Wang, Wei & Hu, Wentao & Shi, Chaojun, 2022. "Economic recovery forecasts under impacts of COVID-19," Economic Modelling, Elsevier, vol. 110(C).
    32. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    33. Rybacki, Jakub & Gniazdowski, Michał, 2021. "Macroeconomic Forecasting in Poland: Lessons From the COVID-19 Outbreak," MPRA Paper 107682, University Library of Munich, Germany.
    34. Suckert, Lisa, 2021. "Von der Pandemie zu einer Neuordnung der Zeit? Zeitsoziologische Perspektiven auf das Verhältnis von Zeitlichkeit, Wirtschaft und Staat," MPIfG Discussion Paper 21/7, Max Planck Institute for the Study of Societies.
    35. Jakub Rybacki & Michał Gniazdowski, 2023. "Macroeconomic forecasting in Poland: lessons from the external shocks," Bank i Kredyt, Narodowy Bank Polski, vol. 54(1), pages 45-64.
    36. Arbolino, Roberta & Caro, Paolo Di, 2021. "Can the EU funds promote regional resilience at time of Covid-19? Insights from the Great Recession11We thank the Editors and the four anonymous referees for helpful comments. We also thank Emanuele C," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 109-126.
    37. Severin Reissl & Alessandro Caiani & Francesco Lamperti & Mattia Guerini & Fabio Vanni & Giorgio Fagiolo & Tommaso Ferraresi & Leonardo Ghezzi & Mauro Napoletano & Andrea Roventini, 2021. "Assessing the economic effects of lockdowns in Italy: a computational Input-Output approach," LEM Papers Series 2021/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    38. Monica Laura Zlati & Costinela Fortea & Alina Meca & Valentin Marian Antohi, 2024. "Approaches to Prognosing the European Economic Crisis Through a New Economic–Financial Risk Sensitivity Model," Economies, MDPI, vol. 13(1), pages 1-30, December.
    39. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2024.
    40. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    41. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    42. İsmail Cakmak & Selcen Öztürk, 2023. "Analysing Impact of Economic Crises on Sector Profits with a New Approach," Prague Economic Papers, Prague University of Economics and Business, vol. 2023(3), pages 225-245.
    43. Archanskaia, Elizaveta & Canton, Erik & Hobza, Alexandr & Nikolov, Plamen & Simons, Wouter, 2023. "The asymmetric impact of COVID-19: A novel approach to quantifying financial distress across industries," European Economic Review, Elsevier, vol. 158(C).
    44. Antonio Oliva & Francesco Gracceva & Daniele Lerede & Matteo Nicoli & Laura Savoldi, 2021. "Projection of Post-Pandemic Italian Industrial Production through Vector AutoRegressive Models," Energies, MDPI, vol. 14(17), pages 1-18, September.
    45. Wang, Yuting & Chen, Heng & Qiao, Shichao & Pan, Peiyuan & Xu, Gang & Dong, Yuehong & Jiang, Xue, 2023. "A novel methanol-electricity cogeneration system based on the integration of water electrolysis and plasma waste gasification," Energy, Elsevier, vol. 267(C).
    46. Fabrizio Iacone & Luca Rossini & Andrea Viselli, 2024. "Comparing predictive ability in presence of instability over a very short time," Papers 2405.11954, arXiv.org.
    47. Nugroho, Anggoro Dimas Pambudi, 2022. "Strategi Ekonomi Bisnis dalam Upaya Menghadapi Ancaman Resesi 2023," OSF Preprints j3dpm, Center for Open Science.
    48. Nugroho, Anggoro Dimas Pambudi, 2022. "Strategi Ekonomi Bisnis dalam Upaya Menghadapi Ancaman Resesi 2023," OSF Preprints j3dpm_v1, Center for Open Science.

  26. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2019. "Assessing International Commonality in Macroeconomic Uncertainty and Its Effects," CEPR Discussion Papers 13970, C.E.P.R. Discussion Papers.

    Cited by:

    1. Jose E. Gomez-Gonzalez & Jorge Hirs-Garzon & Jorge M. Uribe, 2020. "Global effects of US uncertainty: real and financial shocks on real and financial markets," IREA Working Papers 202015, University of Barcelona, Research Institute of Applied Economics, revised Oct 2020.
    2. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    3. Efrem Castelnuovo, 2022. "Uncertainty Before and During COVID-19: A Survey," "Marco Fanno" Working Papers 0279, Dipartimento di Scienze Economiche "Marco Fanno".
    4. Iader Giraldo & Carlos Giraldo & José E. Gomez-Gonzalez & Jorge Mario Uribe, 2023. "US uncertainty shocks, credit, production, and prices: The case of fourteen Latin American countries," Documentos de trabajo 20667, FLAR.
    5. Miescu, Mirela S., 2023. "Uncertainty shocks in emerging economies: A global to local approach for identification," European Economic Review, Elsevier, vol. 154(C).
    6. Nina Biljanovska & Mr. Francesco Grigoli & Martina Hengge, 2017. "Fear Thy Neighbor: Spillovers from Economic Policy Uncertainty," IMF Working Papers 2017/240, International Monetary Fund.
    7. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    8. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    9. Bobasu, Alina & Geis, André & Quaglietti, Lucia & Ricci, Martino, 2021. "Tracking global economic uncertainty: implications for the euro area," Working Paper Series 2541, European Central Bank.
    10. Beckmann, Joscha & Davidson, Sharada Nia & Koop, Gary & Schüssler, Rainer, 2023. "Cross-country uncertainty spillovers: Evidence from international survey data," Journal of International Money and Finance, Elsevier, vol. 130(C).
    11. Andrea Carriero & Alessio Volpicella, 2022. "Generalizing the Max Share Identification to multiple shocks identification: an Application to Uncertainty," School of Economics Discussion Papers 0322, School of Economics, University of Surrey.
    12. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    13. Lodge, David & Pérez, Javier J. & Albrizio, Silvia & Everett, Mary & De Bandt, Olivier & Georgiadis, Georgios & Ca' Zorzi, Michele & Lastauskas, Povilas & Carluccio, Juan & Parraga Rodriguez, Susana &, 2021. "The implications of globalisation for the ECB monetary policy strategy," Occasional Paper Series 263, European Central Bank.
    14. Arigoni, Filippo & Lenarcic, Crt, 2023. "Foreign economic policy uncertainty shocks and real activity in the Euro area," Research Technical Papers 7/RT/23, Central Bank of Ireland.
    15. Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2021. ""Vulnerable Funding in the Global Economy"," IREA Working Papers 202106, University of Barcelona, Research Institute of Applied Economics, revised Mar 2021.
    16. Ogbuabor, Jonathan E. & Ukwueze, Ezebuilo R. & Mba, Ifeoma C. & Ojonta, Obed I. & Orji, Anthony, 2023. "The asymmetric impact of economic policy uncertainty on global retail energy markets: Are the markets responding to the fear of the unknown?," Applied Energy, Elsevier, vol. 334(C).
    17. Andreas Dibiasi & Samad Sarferaz, 2020. "Measuring Macroeconomic Uncertainty: The Labor Channel of Uncertainty from a Cross-Country Perspective," Papers 2006.09007, arXiv.org, revised Dec 2020.
    18. Giovanni Caggiano & Efrem Castelnuovo, 2023. "Global financial uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 432-449, April.
    19. Gomez-Gonzalez, Jose E. & Hirs-Garzon, Jorge & Uribe, Jorge M., 2024. "US uncertainty shocks on real and financial markets: A multi-country perspective," Economic Systems, Elsevier, vol. 48(3).
    20. Graziano Moramarco, 2022. "Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers," Econometrics, MDPI, vol. 11(1), pages 1-29, December.
    21. Jaromir Baxa & Tomas Sestorad, 2024. "Economic Policy Uncertainty in Europe: Spillovers and Common Shocks," Working Papers IES 2024/34, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2024.
    22. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    23. Andreas Dibiasi & Samad Sarferaz, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," Post-Print hal-04167343, HAL.
    24. Ductor, Lorenzo & Leiva-León, Danilo, 2022. "Fluctuations in global output volatility," Journal of International Money and Finance, Elsevier, vol. 120(C).
    25. Bonciani, Dario & Ricci, Martino, 2020. "The international effects of global financial uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 109(C).
    26. Crespo Cuaresma, Jesús & Huber, Florian & Onorante, Luca, 2020. "Fragility and the effect of international uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 108(C).
    27. Paul Labonne, 2020. "Asymmetric uncertainty : Nowcasting using skewness in real-time data," Papers 2012.02601, arXiv.org, revised May 2024.
    28. Wu, Ping, 2024. "Should I open to forecast? Implications from a multi-country unobserved components model with sparse factor stochastic volatility," International Journal of Forecasting, Elsevier, vol. 40(3), pages 903-917.

  27. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Endogenous Uncertainty," Working Papers (Old Series) 1805, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Qazi Haque & Leandro M. Magnusson & Kazuki Tomioka, 2019. "Empirical evidence on the dynamics of investment under uncertainty in the U.S," Economics Discussion / Working Papers 19-18, The University of Western Australia, Department of Economics.
    2. Hauzenberger, Niko & Pfarrhofer, Michael & Stelzer, Anna, 2021. "On the effectiveness of the European Central Bank’s conventional and unconventional policies under uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 822-845.
    3. Maria Elena Bontempi & Michele Frigeri & Roberto Golinelli & Matteo Squadrani, 2021. "EURQ: A New Web Search‐based Uncertainty Index," Economica, London School of Economics and Political Science, vol. 88(352), pages 969-1015, October.
    4. Gian Paulo Soave, 2020. "International Drivers of Policy Uncertainty in Emerging Economies," Economics Bulletin, AccessEcon, vol. 40(1), pages 716-726.

  28. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Working Paper Series 2206, European Central Bank.

    Cited by:

    1. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    2. Sebastian Ankargren & Paulina Jon'eus, 2019. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Papers 1907.01075, arXiv.org.
    3. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    4. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.

  29. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2018. "The global component of inflation volatility," Temi di discussione (Economic working papers) 1170, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Luis J. Álvarez & Maria Dolores Gadea & Ana Gómez‐Loscos, 2021. "Inflation comovements in advanced economies: Facts and drivers," The World Economy, Wiley Blackwell, vol. 44(2), pages 485-509, February.
    2. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    3. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2019. "Domestic and global determinants of inflation: evidence from expectile regression," Temi di discussione (Economic working papers) 1225, Bank of Italy, Economic Research and International Relations Area.
    4. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    5. Ascari, Guido & Fosso, Luca, 2024. "The international dimension of trend inflation," Journal of International Economics, Elsevier, vol. 148(C).
    6. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    7. Francesco Corsello & Alex Tagliabracci, 2023. "Assessing the pass-through of energy prices to inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 745, Bank of Italy, Economic Research and International Relations Area.
    8. Lodge, David & Pérez, Javier J. & Albrizio, Silvia & Everett, Mary & De Bandt, Olivier & Georgiadis, Georgios & Ca' Zorzi, Michele & Lastauskas, Povilas & Carluccio, Juan & Parraga Rodriguez, Susana &, 2021. "The implications of globalisation for the ECB monetary policy strategy," Occasional Paper Series 263, European Central Bank.
    9. Efrem Castelnuovo, 2019. "Domestic and Global Uncertainty: A Survey and Some New Results," CESifo Working Paper Series 7900, CESifo.
    10. İbrahim Özmen & Şerife Özşahin, 2023. "Effects of global energy and price fluctuations on Turkey's inflation: new evidence," Economic Change and Restructuring, Springer, vol. 56(4), pages 2695-2728, August.
    11. Casalin, Fabrizio & Cerniglia, Floriana & Dia, Enzo, 2023. "Stock-flow adjustments, public debt management and interest costs," Economic Modelling, Elsevier, vol. 129(C).
    12. Jiang, Yanhui & Qu, Bo & Hong, Yun & Xiao, Xiyue, 2024. "Dynamic connectedness of inflation around the world: A time-varying approach from G7 and E7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 111-125.
    13. Koirala, Niraj P. & Nyiwul, Linus, 2023. "Inflation volatility: A Bayesian approach," Research in Economics, Elsevier, vol. 77(1), pages 185-201.
    14. Martin Feldkircher & Pierre L. Siklos, 2018. "Global inflation dynamics and inflation expectations," CAMA Working Papers 2018-60, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Lorenzo Burlon & Alessandro Notarpietro & Massimiliano Pisani, 2018. "Exchange rate pass-through into euro area inflation. An estimated structural model," Temi di discussione (Economic working papers) 1192, Bank of Italy, Economic Research and International Relations Area.
    16. Giovanni Caggiano & Efrem Castelnuovo, 2023. "Global financial uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 432-449, April.
    17. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. Stefano Neri & Fabio Busetti & Cristina Conflitti & Francesco Corsello & Davide Delle Monache & Alex Tagliabracci, 2023. "Energy price shocks and inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 792, Bank of Italy, Economic Research and International Relations Area.
    19. Gianluca Cubadda, 2024. "VAR models with an index structure: A survey with new results," Papers 2412.11278, arXiv.org.
    20. Ilaria De Angelis & Guido de Blasio & Lucia Rizzica, 2018. "On the unintended effects of public transfers: evidence from EU funding to Southern Italy," Temi di discussione (Economic working papers) 1180, Bank of Italy, Economic Research and International Relations Area.
    21. Stefano Neri & Stefano Siviero, 2019. "The non-standard monetary policy measures of the ECB: motivations, effectiveness and risks," Questioni di Economia e Finanza (Occasional Papers) 486, Bank of Italy, Economic Research and International Relations Area.
    22. Luis J. Álvarez & Ana Gómez-Loscos & María Dolores Gadea, 2019. "Inflation interdependence in advanced economies," Working Papers 1920, Banco de España.
    23. Aysun, Uluc & Wright, Cardel, 2024. "A two-step dynamic factor modelling approach for forecasting inflation in small open economies," Emerging Markets Review, Elsevier, vol. 62(C).

  30. Dario Buono & George Kapetanios & Massimiliano Marcellino & Gianluigi Mazzi & Fotis Papailias, 2018. "Big Data Econometrics: Now Casting and Early Estimates," BAFFI CAREFIN Working Papers 1882, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    2. Fornaro, Paolo, 2020. "Nowcasting Industrial Production Using Uncoventional Data Sources," ETLA Working Papers 80, The Research Institute of the Finnish Economy.
    3. Irving Fisher Committee, 2023. "Data science in central banking: applications and tools," IFC Bulletins, Bank for International Settlements, number 59.
    4. George Kapetanios & Fotis Papailias, 2022. "Investigating the predictive ability of ONS big data‐based indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 252-258, March.
    5. Francisco Corona & Graciela Gonz'alez-Far'ias & Jes'us L'opez-P'erez, 2021. "A nowcasting approach to generate timely estimates of Mexican economic activity: An application to the period of COVID-19," Papers 2101.10383, arXiv.org.

  31. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.

    Cited by:

    1. Pedro Isaac Chavez-Lopez & Tae-Hwy Lee, 2025. "Quantile-Covariance Three-Pass Regression Filter," Working Papers 202501, University of California at Riverside, Department of Economics.
    2. Elie Bouri & Christina Christou & Rangan Gupta, 2022. "Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models," Working Papers 202213, University of Pretoria, Department of Economics.
    3. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.

  32. Marcellino, Massimiliano & Bertolotti, Fabio, 2017. "Tax shocks with high and low uncertainty," CEPR Discussion Papers 12335, C.E.P.R. Discussion Papers.

    Cited by:

    1. Eller, Markus & Hauzenberger, Niko & Huber, Florian & Schuberth, Helene & Vashold, Lukas, 2021. "The impact of macroprudential policies on capital flows in CESEE," Journal of International Money and Finance, Elsevier, vol. 119(C).
    2. Jerow, Sam & Wolff, Jonathan, 2022. "Fiscal policy and uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).
    3. Sangyup Choi & Junhyeok Shin, 2020. "Household Indebtedness and the Macroeconomic Effects of Tax Changes," Working papers 2020rwp-178, Yonsei University, Yonsei Economics Research Institute.
    4. K. Peren Arin & Kevin Devereux & Mieszko Mazur, 2021. "Taxes and firm investment," CAMA Working Papers 2021-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

  33. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    2. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    3. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
    4. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    5. Petrella, Ivan & Delle Monache, Davide & Venditti, Fabrizio, 2019. "Price Dividend Ratio and Long-Run Stock Returns: a Score Driven State Space Model," CEPR Discussion Papers 14107, C.E.P.R. Discussion Papers.
    6. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    7. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    8. Giacomo Rella, 2021. "The Fed, housing and household debt over time," Department of Economics University of Siena 850, Department of Economics, University of Siena.
    9. Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    10. Bai, Yu & Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022. "Macroeconomic Forecasting in a Multi-country Context," CEPR Discussion Papers 16994, C.E.P.R. Discussion Papers.
    11. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    12. César Castro & Rebeca Jiménez-Rodríguez, 2020. "Dynamic interactions between oil price and exchange rate," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.
    13. Zheng, Tingguo & Ye, Shiqi & Hong, Yongmiao, 2023. "Fast estimation of a large TVP-VAR model with score-driven volatilities," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    14. Rajae Azrak & Guy Mélard, 2022. "Autoregressive Models with Time-Dependent Coefficients—A Comparison between Several Approaches," Stats, MDPI, vol. 5(3), pages 1-21, August.
    15. Jiménez-Rodríguez, Rebeca, 2022. "Oil shocks and global economy," Energy Economics, Elsevier, vol. 115(C).
    16. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    17. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    18. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    19. Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.
    20. S. Avouyi-Dovi & C. Labonne & R. Lecat & S. Ray, 2017. "Insight from a Time-Varying VAR Model with Stochastic Volatility of the French Housing and Credit Markets," Working papers 620, Banque de France.
    21. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).

  34. Angela Abbate & Massimiliano Marcellino, 2017. "Macroeconomic activity and risk indicators: an unstable relationship," BAFFI CAREFIN Working Papers 1756, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Francesco Corsello & Valerio Nispi Landi, 2020. "Labor Market and Financial Shocks: A Time‐Varying Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 777-801, June.

  35. Claudia Foroni & Pierre Guérin & Massimiliano Marcellino, 2017. "Explaining the Time-varying Effects Of Oil Market Shocks On U.S. Stock Returns," Working Papers 597, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Bhaskar Bagchi & Biswajit Paul, 2023. "Effects of Crude Oil Price Shocks on Stock Markets and Currency Exchange Rates in the Context of Russia-Ukraine Conflict: Evidence from G7 Countries," JRFM, MDPI, vol. 16(2), pages 1-18, January.
    2. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    3. Ron Alquist & Reinhard Ellwanger & Jianjian Jin, 2020. "The effect of oil price shocks on asset markets: Evidence from oil inventory news," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(8), pages 1212-1230, August.
    4. Sa Xu & Ziqing Du & Hai Zhang, 2020. "Can Crude Oil Serve as a Hedging Asset for Underlying Securities?—Research on the Heterogenous Correlation between Crude Oil and Stock Index," Energies, MDPI, vol. 13(12), pages 1-19, June.
    5. Mushtaq Hussain Khan & Junaid Ahmed & Mazhar Mughal & Imtiaz Hussain Khan, 2023. "Oil price volatility and stock returns: Evidence from three oil‐price wars," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3162-3182, July.
    6. Alexey Mikhaylov & Ishaq M. Bhatti & Hasan Dinçer & Serhat Yüksel, 2024. "Integrated decision recommendation system using iteration-enhanced collaborative filtering, golden cut bipolar for analyzing the risk-based oil market spillovers," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 305-338, January.
    7. Liu, Zhenhua & Tseng, Hui-Kuan & Wu, Jy S. & Ding, Zhihua, 2020. "Implied volatility relationships between crude oil and the U.S. stock markets: Dynamic correlation and spillover effects," Resources Policy, Elsevier, vol. 66(C).
    8. Mohammad Sharik Essa & Evangelos Giouvris, 2020. "Oil Price, Oil Price Implied Volatility (OVX) and Illiquidity Premiums in the US: (A)symmetry and the Impact of Macroeconomic Factors," JRFM, MDPI, vol. 13(4), pages 1-40, April.
    9. Liu, Zhenhua & Shi, Xunpeng & Zhai, Pengxiang & Wu, Shan & Ding, Zhihua & Zhou, Yuqin, 2021. "Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach," Resources Policy, Elsevier, vol. 74(C).
    10. Huang, Wanling & Mollick, Andre Varella, 2020. "Tight oil, real WTI prices and U.S. stock returns," Energy Economics, Elsevier, vol. 85(C).
    11. Le, Thai-Ha & Le, Anh Tu & Le, Ha-Chi, 2021. "The historic oil price fluctuation during the Covid-19 pandemic: What are the causes?," Research in International Business and Finance, Elsevier, vol. 58(C).
    12. Arampatzidis, Ioannis & Panagiotidis, Theodore, 2023. "On the identification of the oil-stock market relationship," Economic Modelling, Elsevier, vol. 120(C).
    13. Zeina Alsalman, 2021. "Does the source of oil supply shock matter in explaining the behavior of U.S. consumer spending and sentiment?," Empirical Economics, Springer, vol. 61(3), pages 1491-1518, September.
    14. Brice V. Dupoyet & Corey A. Shank, 2018. "Oil prices implied volatility or direction: Which matters more to financial markets?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(3), pages 275-295, August.
    15. Jiang, Yong & Wang, Gang-Jin & Ma, Chaoqun & Yang, Xiaoguang, 2021. "Do credit conditions matter for the impact of oil price shocks on stock returns? Evidence from a structural threshold VAR model," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 1-15.
    16. Ioannis Arampatzidis & Theologos Dergiades & Robert. K. Kaufmann & Theodore Panagiotidis, 2021. "Oil and the U.S. Stock Market: Implications for Low Carbon Policies," Working Paper series 21-19, Rimini Centre for Economic Analysis.
    17. Yousaf, Imran & Beljid, Makram & Chaibi, Anis & Ajlouni, Ahmed AL, 2022. "Do volatility spillover and hedging among GCC stock markets and global factors vary from normal to turbulent periods? Evidence from the global financial crisis and Covid-19 pandemic crisis," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    18. Martínez-Cañete, Ana R. & Márquez-de-la-Cruz, Elena & Pérez-Soba, Inés, 2022. "Non-linear cointegration between oil and stock prices: The role of interest rates," Research in International Business and Finance, Elsevier, vol. 59(C).
    19. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.
    20. Eraslan, Sercan & Menla Ali, Faek, 2018. "Oil price shocks and stock return volatility: New evidence based on volatility impulse response analysis," Economics Letters, Elsevier, vol. 172(C), pages 59-62.
    21. Chen, Shiu-Sheng & Huang, Shiangtsz & Lin, Tzu-Yu, 2022. "How do oil prices affect emerging market sovereign bond spreads?," Journal of International Money and Finance, Elsevier, vol. 128(C).
    22. Zhenhua Liu & Zhihua Ding & Tao Lv & Jy S. Wu & Wei Qiang, 2019. "Financial factors affecting oil price change and oil-stock interactions: a review and future perspectives," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 207-225, January.
    23. Eraslan, Sercan & Ali, Faek Menla, 2018. "Oil price shocks and stock return volatility: New evidence based on volatility impulse response analysis," Discussion Papers 38/2018, Deutsche Bundesbank.

  36. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Measuring Uncertainty and Its Impact on the Economy," BAFFI CAREFIN Working Papers 1639, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Gian Paulo Soave, 2023. "A panel threshold VAR with stochastic volatility-in-mean model: an application to the effects of financial and uncertainty shocks in emerging economies," Applied Economics, Taylor & Francis Journals, vol. 55(4), pages 397-431, January.
    2. Laurent Ferrara & Stéphane Lhuissier & Fabien Tripier, 2017. "Uncertainty Fluctuations: Measures, Effects and Macroeconomic Policy Challenges," CEPII Policy Brief 2017-20, CEPII research center.
    3. Giovanni Caggiano & Efrem Castelnuovo & Juan Manuel Figueres, 2018. "Economic Policy Uncertainty Spillovers in Booms and Busts," "Marco Fanno" Working Papers 0220, Dipartimento di Scienze Economiche "Marco Fanno".
    4. Ma, Xiaohan & Samaniego, Roberto, 2020. "The macroeconomic impact of oil earnings uncertainty: New evidence from analyst forecasts," Energy Economics, Elsevier, vol. 90(C).
    5. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    6. Danilo Leiva-Leon & Luis Uzeda, 2020. "Endogenous Time Variation in Vector Autoregressions," Staff Working Papers 20-16, Bank of Canada.
    7. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    8. Fischer, Manfred M. & Huber, Florian & Pfarrhofer, Michael, 2018. "The transmission of uncertainty shocks on income inequality: State-level evidence from the United States," Working Papers in Regional Science 2018/06, WU Vienna University of Economics and Business.
    9. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Aygun, Gurcan & Wohar, Mark E., 2022. "The macroeconomic impact of economic uncertainty and financial shocks under low and high financial stress," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    11. Yoosoon Chang & Ana María Herrera & Elena Pesavento, 2023. "Oil Prices Uncertainty, Endogenous Regime Switching, and Inflation Anchoring," CAMA Working Papers 2023-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Xianbo Zhou & Zhuoran Chen, 2023. "The Impact of Uncertainty Shocks to Consumption under Different Confidence Regimes Based on a Stochastic Uncertainty-in-Mean TVAR Model," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    13. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    14. Karamysheva, Madina, 2022. "How do fiscal adjustments work? An empirical investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    15. Fabio Bertolotti & Massimiliano Marcellino, 2019. "Tax shocks with high and low uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 972-993, September.
    16. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The Real Effects of Financial Uncertainty Shocks: A Daily Identification Approach," Documentos de Trabajo 559, Instituto de Economia. Pontificia Universidad Católica de Chile..
    17. Magnus, Jan R. & Pijls, Henk G.J. & Sentana, Enrique, 2021. "The Jacobian of the exponential function," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    18. Joshua C. C. Chan, 2024. "BVARs and stochastic volatility," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 3, pages 43-67, Edward Elgar Publishing.
    19. Hernández Vega Marco A., 2021. "The Nonlinear Effect of Uncertainty in Portfolio Flows to Mexico," Working Papers 2021-11, Banco de México.
    20. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage," CAMA Working Papers 2019-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    21. Popiel Michal Ksawery, 2020. "Fiscal policy uncertainty and US output," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-26, April.
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    60. Danilo Cascaldi-Garcia & Cisil Sarisoy & Juan M. Londono & Bo Sun & Deepa D. Datta & Thiago Ferreira & Olesya Grishchenko & Mohammad R. Jahan-Parvar & Francesca Loria & Sai Ma & Marius Rodriguez & Ilk, 2023. "What Is Certain about Uncertainty?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 624-654, June.
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    100. Berger, Tino & Kempa, Bernd & Zou, Feina, 2023. "The role of macroeconomic uncertainty in the determination of the natural rate of interest," Economics Letters, Elsevier, vol. 229(C).
    101. Josué Diwambuena & Jean-Paul K. Tsasa, 2021. "The Real Effects of Uncertainty Shocks: New Evidence from Linear and Nonlinear SVAR Models," BEMPS - Bozen Economics & Management Paper Series BEMPS87, Faculty of Economics and Management at the Free University of Bozen.
    102. Luo, Jiawen & Marfatia, Hardik A. & Ji, Qiang & Klein, Tony, 2023. "Co-volatility and asymmetric transmission of risks between the global oil and China's futures markets," Energy Economics, Elsevier, vol. 117(C).
    103. Ma, Xiaohan & Samaniego, Roberto, 2019. "Deconstructing uncertainty," European Economic Review, Elsevier, vol. 119(C), pages 22-41.
    104. Cristiana Fiorelli & Alfredo Cartone & Matteo Foglia, 2021. "Shadow rates and spillovers across the Eurozone: a spatial dynamic panel model," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(1), pages 223-245, February.
    105. Martin Iseringhausen & Konstantinos Theodoridis, 2025. "A survey-based measure of asymmetric macroeconomic risk in the euro area," Working Papers 68, European Stability Mechanism, revised 11 Feb 2025.
    106. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2023. "Are the Effects of Uncertainty Shocks Big or Small?," Working Papers 244, Red Nacional de Investigadores en Economía (RedNIE).
    107. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.
    108. Fuest, Angela & Schmidt, Torsten, 2020. "Inflation expectation uncertainty in a New Keynesian framework," Ruhr Economic Papers 867, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    109. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    110. Schüler, Yves S., 2020. "The impact of uncertainty and certainty shocks," Discussion Papers 14/2020, Deutsche Bundesbank.
    111. Myriam Gómez-Méndez & Erwin Hansen, 2021. "Economic policy uncertainty and presidential approval: Evidence from Latin America," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-17, March.
    112. Timo Wollmershäuser & Florian Eckert & Marcell Göttert & Christian Grimme & Carla Krolage & Stefan Lautenbacher & Robert Lehmann & Sebastian Link & Heiner Mikosch & Stefan Neuwirth & Wolfgang Nierhaus, 2019. "ifo Konjunkturprognose Winter 2019: Deutsche Konjunktur stabilisiert sich," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(24), pages 27-89, December.
    113. Tong, Chen & Huang, Zhuo & Wang, Tianyi & Zhang, Cong, 2023. "The effects of economic uncertainty on financial volatility: A comprehensive investigation," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 369-389.
    114. Alessio Anzuini & Luca Rossi, 2021. "Fiscal policy in the US: a new measure of uncertainty and its effects on the American economy," Empirical Economics, Springer, vol. 61(5), pages 2613-2634, November.
    115. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2020. "Revising the Impact of Financial and Non-Financial Global Stock Market Volatility Shocks," MPRA Paper 103019, University Library of Munich, Germany.
    116. Juan M. Londono & Sai Ma & Beth Anne Wilson, 2021. "The Global Transmission of Real Economic Uncertainty," International Finance Discussion Papers 1317, Board of Governors of the Federal Reserve System (U.S.).
    117. Lin Liu, 2021. "U.S. Economic Uncertainty Shocks and China’s Economic Activities: A Time-Varying Perspective," SAGE Open, , vol. 11(3), pages 21582440211, July.
    118. Yao, Shouyu & Liu, Zezhong & Wang, Chunfeng & Palma, Alessia & Goodell, John W., 2024. "Is macroeconomic tail risk contagious to stock idiosyncratic risk?," Finance Research Letters, Elsevier, vol. 63(C).
    119. Bae, Siye & Jo, Soojin & Shim, Myungkyu, 2023. "United States of Mind under Uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 102-127.
    120. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    121. Jamal Bouoiyour & Refk Selmi, 2019. "The Qatar-Gulf Crisis and Risk Management in Oil and Gas Markets," Working Papers hal-02101633, HAL.
    122. Oscar Claveria, 2021. "Disagreement on expectations: firms versus consumers," SN Business & Economics, Springer, vol. 1(12), pages 1-23, December.
    123. Gorodnichenko, Yuriy & Ng, Serena, 2017. "Level and volatility factors in macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 52-68.
    124. Andreas Dibiasi & Samad Sarferaz, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," Post-Print hal-04167343, HAL.
    125. Yunmi Kim & Tae-Hwan Kim, 2024. "Generalized Impulse and Its Measure," Working papers 2024rwp-226, Yonsei University, Yonsei Economics Research Institute.
    126. Ductor, Lorenzo & Leiva-León, Danilo, 2022. "Fluctuations in global output volatility," Journal of International Money and Finance, Elsevier, vol. 120(C).
    127. M. Karanasos & S. Yfanti & J. Hunter, 2022. "Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises," Annals of Operations Research, Springer, vol. 313(2), pages 1077-1116, June.
    128. Christian Glocker & Werner Hölzl, 2019. "Assessing the Economic Content of Direct and Indirect Business Uncertainty Measures," WIFO Working Papers 576, WIFO.
    129. Crespo Cuaresma, Jesús & Huber, Florian & Onorante, Luca, 2020. "Fragility and the effect of international uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 108(C).
    130. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    131. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    132. Awijen, Haithem & Ben Zaied, Younes & Nguyen, Duc Khuong & Sensoy, Ahmet, 2020. "Endogenous Financial Uncertainty and Macroeconomic Volatility: Evidence from the United States," MPRA Paper 101276, University Library of Munich, Germany, revised Jun 2020.
    133. Paul Labonne, 2020. "Asymmetric uncertainty : Nowcasting using skewness in real-time data," Papers 2012.02601, arXiv.org, revised May 2024.
    134. Asad Dossani & John Elder, 2024. "Uncertainty and investment: Evidence from domestic oil rigs," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 323-340, February.
    135. Kim C. Raath & Katherine B. Ensor, 2023. "Wavelet-L2E Stochastic Volatility Models: an Application to the Water-Energy Nexus," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 150-176, May.
    136. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2020. "Measuring Uncertainty and Its Effects in the COVID-19 Era," Working Papers 20-32R, Federal Reserve Bank of Cleveland, revised 05 Jan 2022.
    137. Takao Asano & Xiaojing Cai & Ryuta Sakemoto, 2023. "Time-varying ambiguity shocks and business cycles," KIER Working Papers 1094, Kyoto University, Institute of Economic Research.
    138. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.
    139. Oscar Claveria & Petar Sorić, 2023. "Labour market uncertainty after the irruption of COVID-19," Empirical Economics, Springer, vol. 64(4), pages 1897-1945, April.
    140. Dario Caldara & Chiara Scotti & Molin Zhong, 2021. "Macroeconomic and Financial Risks: A Tale of Mean and Volatility," International Finance Discussion Papers 1326, Board of Governors of the Federal Reserve System (U.S.).
    141. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2022. "When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    142. Schläger, Dan, 2024. "Unmasking the significance of uncertainty: a case study of the German interwar economy (1919-1935)," LSE Research Online Documents on Economics 125837, London School of Economics and Political Science, LSE Library.
    143. Rivolta, Giulia & Trecroci, Carmine, 2020. "Measuring the effects of U.S. uncertainty and monetary conditions on EMEs' macroeconomic dynamics," MPRA Paper 99403, University Library of Munich, Germany.

  37. Christian Hepenstrick & Massimiliano Marcellino, 2016. "Forecasting with Large Unbalanced Datasets: The Mixed-Frequency Three-Pass Regression Filter," Working Papers 2016-04, Swiss National Bank.

    Cited by:

    1. Hager Ben Romdhane, 2021. "Nowcasting in Tunisia using large datasets and mixed frequency models," IHEID Working Papers 11-2021, Economics Section, The Graduate Institute of International Studies.
    2. Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019. "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, vol. 15(2), pages 151-178, June.
    3. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    4. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-switching three-pass regression filter," Working Papers 1748, Banco de España.
    5. Hagher Ben Rhomdhane & Brahim Mehdi Benlallouna, 2022. "Nowcasting real GDP in Tunisia using large datasets and mixed-frequency models," IHEID Working Papers 02-2022, Economics Section, The Graduate Institute of International Studies.
    6. Masud Alam, 2024. "Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach," Empirical Economics, Springer, vol. 67(4), pages 1421-1471, October.

  38. Marcellino, Massimiliano & Abbate, Angela, 2016. "Point, interval and density forecasts of exchange rates with time-varying parameter models," CEPR Discussion Papers 11559, C.E.P.R. Discussion Papers.

    Cited by:

    1. Angela Abbate & Massimiliano Marcellino, 2017. "Macroeconomic activity and risk indicators: an unstable relationship," BAFFI CAREFIN Working Papers 1756, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    2. Hauzenberger, Niko & Huber, Florian, 2018. "Model instability in predictive exchange rate regressions," Department of Economics Working Paper Series 276, WU Vienna University of Economics and Business.
    3. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    4. Justyna Wróblewska & Anna Pajor, 2019. "One-period joint forecasts of Polish inflation, unemployment and interest rate using Bayesian VEC-MSF models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 11(1), pages 23-45, March.
    5. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    6. Papahristodoulou, Christos, 2019. "Is there any theory that explains the SEK?," MPRA Paper 95072, University Library of Munich, Germany, revised 08 Jul 2019.
    7. Aristidou, Chrystalleni & Lee, Kevin & Shields, Kalvinder, 2022. "Fundamentals, regimes and exchange rate forecasts: Insights from a meta exchange rate model," Journal of International Money and Finance, Elsevier, vol. 123(C).
    8. Anna Pajor & Justyna Wróblewska, 2022. "Forecasting performance of Bayesian VEC-MSF models for financial data in the presence of long-run relationships," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 427-448, September.
    9. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    10. Krystian Jaworski, 2021. "Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 977-999, September.
    11. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
    12. Beckmann, J & Koop, G & Korobilis, D & Schüssler, R, 2017. "Exchange rate predictability and dynamic Bayesian learning," Essex Finance Centre Working Papers 20781, University of Essex, Essex Business School.
    13. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    14. Anna Pajor & Justyna Wróblewska & Łukasz Kwiatkowski & Jacek Osiewalski, 2024. "Hybrid SV‐GARCH, t‐GARCH and Markov‐switching covariance structures in VEC models—Which is better from a predictive perspective?," International Statistical Review, International Statistical Institute, vol. 92(1), pages 62-86, April.
    15. Adebayo Felix Adekoya & Isaac Kofi Nti & Benjamin Asubam Weyori, 2021. "Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi," FinTech, MDPI, vol. 1(1), pages 1-19, December.
    16. Camba-Méndez, Gonzalo, 2020. "On the inflation risks embedded in sovereign bond yields," Working Paper Series 2423, European Central Bank.

  39. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    2. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    3. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    4. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    5. Harry Turunen & Anastasia Zhutova & Matthieu Lemoine, 2023. "Stochastic Simulation of the FR-BDF Model and an Assessment of Uncertainty around Conditional Forecasts," Working papers 920, Banque de France.
    6. Joshua C.C. Chan & Eric Eisenstat & Rodney W. Strachan, 2018. "Reducing dimensions in a large TVP-VAR," CAMA Working Papers 2018-49, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1658-1668.
    8. Bognanni, Mark, 2022. "Comment on “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors”," Journal of Econometrics, Elsevier, vol. 227(2), pages 498-505.
    9. Petrova, Katerina, 2022. "Asymptotically valid Bayesian inference in the presence of distributional misspecification in VAR models," Journal of Econometrics, Elsevier, vol. 230(1), pages 154-182.
    10. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    11. Lu, Yunzhi & Li, Jie & Yang, Haisheng, 2023. "Time-varying impacts of monetary policy uncertainty on China's housing market," Economic Modelling, Elsevier, vol. 118(C).
    12. Dellaportas, Petros & Titsias, Michalis K. & Petrova, Katerina & Plataniotis, Anastasios, 2023. "Scalable inference for a full multivariate stochastic volatility model," Journal of Econometrics, Elsevier, vol. 232(2), pages 501-520.

  40. Claudia Foroni & Pierre Guérin & Massimiliano Marcellino, 2015. "Using low frequency information for predicting high frequency variables," Working Paper 2015/13, Norges Bank.

    Cited by:

    1. Tang, Zhenpeng & Lin, Qiaofeng & Cai, Yi & Chen, Kaijie & Liu, Dinggao, 2024. "Harnessing the power of real-time forum opinion: Unveiling its impact on stock market dynamics using intraday high-frequency data in China," International Review of Financial Analysis, Elsevier, vol. 93(C).
    2. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    3. Layna Mosley & Victoria Paniagua & Erik Wibbels, 2020. "Moving markets? Government bond investors and microeconomic policy changes," Economics and Politics, Wiley Blackwell, vol. 32(2), pages 197-249, July.
    4. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Economic Modelling, Elsevier, vol. 120(C).
    5. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    6. Xu, Qifa & Li, Mengting & Jiang, Cuixia & He, Yaoyao, 2019. "Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    7. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting Realized US Stock Market Volatility: Is there a Role for Economic Policy Uncertainty?," Working Papers 202408, University of Pretoria, Department of Economics.
    8. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    9. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    10. Joel Hasbrouck, 2021. "Rejoinder on: Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 465-471.
    11. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
    12. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-switching three-pass regression filter," Working Papers 1748, Banco de España.
    13. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
    14. Elie Bouri & Rangan Gupta & Luca Rossini, 2022. "The Role of the Monthly ENSO in Forecasting the Daily Baltic Dry Index," Working Papers 202229, University of Pretoria, Department of Economics.
    15. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2025. "The Role of Uncertainty in Forecasting Realized Covariance of US State-Level Stock Returns: A Reverse-MIDAS Approach," Working Papers 202501, University of Pretoria, Department of Economics.
    16. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and Forecastability of the Realized Variance of Agricultural Commodity Prices: Evidence from a Machine Learning Approach," Working Papers 202179, University of Pretoria, Department of Economics.
    17. Mei, Xueting & Wang, Xinyu, 2024. "Forecasting stock volatility using time-distance weighting fundamental’s shocks," Finance Research Letters, Elsevier, vol. 65(C).
    18. Goodness C. Aye & Christina Christou & Rangan Gupta & Christis Hassapis, 2024. "High-Frequency Contagion between Aggregate and Regional Housing Markets of the United States with Financial Assets: Evidence from Multichannel Tests," The Journal of Real Estate Finance and Economics, Springer, vol. 69(2), pages 253-276, August.
    19. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
    20. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    21. Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022. "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, vol. 114(C).
    22. Wenfeng Ma & Yuxuan Hong & Yuping Song, 2024. "On Stock Volatility Forecasting under Mixed-Frequency Data Based on Hybrid RR-MIDAS and CNN-LSTM Models," Mathematics, MDPI, vol. 12(10), pages 1-21, May.
    23. Rangan Gupta & Sarah Nandnaba & Wei Jiang, 2024. "Climate Change and Growth Dynamics," Working Papers 202404, University of Pretoria, Department of Economics.
    24. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    25. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    26. Alexander, Carol & Rauch, Johannes, 2021. "A general property for time aggregation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 536-548.
    27. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    28. Agostino Consolo & Claudia Foroni & Catalina Martínez Hernández, 2023. "A Mixed Frequency BVAR for the Euro Area Labour Market," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 1048-1082, October.
    29. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.
    30. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    31. You, Yu & Liu, Xiaochun, 2020. "Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach," Journal of Banking & Finance, Elsevier, vol. 116(C).
    32. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    33. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    34. Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.
    35. Selma Toker & Nimet Özbay & Kristofer Månsson, 2022. "Mixed data sampling regression: Parameter selection of smoothed least squares estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 718-751, July.
    36. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.

  41. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2015. "Structural Analysis with Multivariate Autoregressive Index Models," CEPR Discussion Papers 10801, C.E.P.R. Discussion Papers.

    Cited by:

    1. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    2. Gianluca Cubadda & Marco Mazzali, 2024. "The vector error correction index model: representation, estimation and identification," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 126-150.
    3. Alain Hecq & Ivan Ricardo & Ines Wilms, 2024. "Reduced-Rank Matrix Autoregressive Models: A Medium $N$ Approach," Papers 2407.07973, arXiv.org.
    4. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    5. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    6. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    7. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    8. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Domenico Sartore, 2018. "A scoring rule for factor and autoregressive models under misspecification," Working Papers 2018:18, Department of Economics, University of Venice "Ca' Foscari".
    9. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    10. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2018. "The global component of inflation volatility," Temi di discussione (Economic working papers) 1170, Bank of Italy, Economic Research and International Relations Area.
    11. Monica Billio & Roberto Casarin & Sylvia Kaufmann & Matteo Iacopini, 2018. "Bayesian Dynamic Tensor Regression," Working Papers 2018:13, Department of Economics, University of Venice "Ca' Foscari".
    12. Mike G. Tsionas, 2016. "Alternative Bayesian compression in Vector Autoregressions and related models," Working Papers 216, Bank of Greece.
    13. Mike G. Tsionas, 2016. "Alternatives to large VAR, VARMA and multivariate stochastic volatility models," Working Papers 217, Bank of Greece.
    14. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
    15. Gianluca Cubadda, 2024. "VAR models with an index structure: A survey with new results," Papers 2412.11278, arXiv.org.

  42. Marcellino, Massimiliano & Sivec, Vasja, 2015. "Monetary, Fiscal and Oil Shocks: Evidence based on Mixed Frequency Structural FAVARs," CEPR Discussion Papers 10610, C.E.P.R. Discussion Papers.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Measuring Uncertainty and Its Impact on the Economy," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 799-815, December.
    2. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    3. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2021. "Measuring the effectiveness of US monetary policy during the COVID‐19 recession," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(3), pages 287-297, July.
    4. Simon Beyeler & Sylvia Kaufmann, 2016. "Factor augmented VAR revisited - A sparse dynamic factor model approach," Working Papers 16.08, Swiss National Bank, Study Center Gerzensee.
    5. Quan Yonghui & Miao Wenlong, 2025. "Asymmetric risk contagion effect of the interaction between the real economy and the financial sector—an analysis based on the domestic commodity price index," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-26, December.
    6. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    7. Fu Qiao & Yan Yan, 2020. "How does stock market reflect the change in economic demand? A study on the industry-specific volatility spillover networks of China's stock market during the outbreak of COVID-19," Papers 2007.07487, arXiv.org.
    8. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    9. Simon Beyeler & Sylvia Kaufmann, 2021. "Reduced‐form factor augmented VAR—Exploiting sparsity to include meaningful factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 989-1012, November.
    10. Franz Ramsauer & Aleksey Min & Michael Lingauer, 2019. "Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components," Econometrics, MDPI, vol. 7(3), pages 1-43, July.
    11. Si, Deng-Kui & Li, Xiao-Lin & Xu, XuChuan & Fang, Yi, 2021. "The risk spillover effect of the COVID-19 pandemic on energy sector: Evidence from China," Energy Economics, Elsevier, vol. 102(C).
    12. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    13. Chen, Chuanglian & Zhou, Lichao & Sun, Chuanwang & Lin, Yuting, 2024. "Does oil future increase the network systemic risk of financial institutions in China?," Applied Energy, Elsevier, vol. 364(C).
    14. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.

  43. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2015. "An Overview of the Factor-augmented Error-Correction Model," Discussion Papers 15-03, Department of Economics, University of Birmingham.

    Cited by:

    1. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    2. Francesca Di Iorio & Stefano Fachin, 2017. "Evaluating Restricted Common Factor models for non-stationary data," DSS Empirical Economics and Econometrics Working Papers Series 2017/2, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    3. Hidayat, Ariodillah & Robiani, Bernadette & Marwa, Taufiq & Suhel, Suhel & Susetyo, Didik & Mukhlis, Mukhlis, 2024. "A Crude Palm Oil Industry Concentration and Influencing Factors: A Case Study of Indonesia as the World's Largest Producer," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 16(01), March.
    4. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    5. Tobias Hartl, 2020. "Macroeconomic Forecasting with Fractional Factor Models," Papers 2005.04897, arXiv.org.
    6. Stoupos, Nikolaos & Nikas, Christos & Kiohos, Apostolos, 2023. "Turkey: From a thriving economic past towards a rugged future? - An empirical analysis on the Turkish financial markets," Emerging Markets Review, Elsevier, vol. 54(C).

  44. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Large Vector Autoregressions with Asymmetric Priors," Working Papers 759, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Feldkircher, Martin & Gruber, Thomas & Huber, Florian, 2017. "Spreading the word or reducing the term spread? Assessing spillovers from euro area monetary policy," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168111, Verein für Socialpolitik / German Economic Association.
    2. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    3. Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," Working Paper series 18-38, Rimini Centre for Economic Analysis.
    4. Martin Feldkircher & Florian Huber, 2016. "Unconventional US Monetary Policy: New Tools, Same Channels?," Department of Economics Working Papers wuwp222, Vienna University of Economics and Business, Department of Economics.
    5. Martin Feldkircher & Elizaveta Lukmanova & Gabriele Tondl, 2019. "Global Factors Driving Inflation and Monetary Policy: A Global VAR Assessment," Department of Economics Working Papers wuwp289, Vienna University of Economics and Business, Department of Economics.
    6. Christian Hotz-Behofsits & Florian Huber & Thomas O. Zorner, 2018. "Predicting crypto-currencies using sparse non-Gaussian state space models," Papers 1801.06373, arXiv.org, revised Feb 2018.
    7. Florian Huber & Thomas Zörner, 2017. "Threshold cointegration and adaptive shrinkage," Department of Economics Working Papers wuwp250, Vienna University of Economics and Business, Department of Economics.
    8. Feldkircher, Martin & Gruber, Thomas & Huber, Florian, 2020. "International effects of a compression of euro area yield curves," Journal of Banking & Finance, Elsevier, vol. 113(C).
    9. Pappa, Evi & Molteni, Francesco, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," CEPR Discussion Papers 12541, C.E.P.R. Discussion Papers.
    10. Gregor Kastner & Florian Huber, 2017. "Sparse Bayesian vector autoregressions in huge dimensions," Papers 1704.03239, arXiv.org, revised Dec 2019.
    11. Emmanuel C. Mamatzakis & Steven Ongena & Mike G. Tsionas, 2023. "The response of household debt to COVID-19 using a neural networks VAR in OECD," Empirical Economics, Springer, vol. 65(1), pages 65-91, July.
    12. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    13. Assaf, A. George & Tsionas, Mike G., 2019. "Forecasting occupancy rate with Bayesian compression methods," Annals of Tourism Research, Elsevier, vol. 75(C), pages 439-449.

  45. Stefano Grassi & Tommaso Proietti & Cecilia Frale & Massimiliano Marcellino & Gianluigi Mazzi, 2014. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro," Studies in Economics 1406, School of Economics, University of Kent.

    Cited by:

    1. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
    2. Joan Paredes & Javier J. Pérez & Gabriel Perez Quiros, 2023. "Fiscal targets. A guide to forecasters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 472-492, June.
    3. María Gil & Javier J. Pérez & Alberto Urtasun, 2019. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.

  46. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Structural FECM: Cointegration in large-scale structural FAVAR models," CEPR Discussion Papers 9858, C.E.P.R. Discussion Papers.

    Cited by:

    1. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Pedro José Piqueras Martínez, 2024. "Dynamic Factor Models and Fractional Integration – With an Application to US Real Economic Activity," CESifo Working Paper Series 11486, CESifo.
    2. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    3. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).
    4. Onatski, Alexei & Wang, Chen, 2019. "Extreme canonical correlations and high-dimensional cointegration analysis," Journal of Econometrics, Elsevier, vol. 212(1), pages 307-322.
    5. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    6. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    7. Favero, Carlo A. & Melone, Alessandro, 2020. "Asset Pricing vs Asset Expected Returning in Factor-Portfolio Models," CEPR Discussion Papers 14417, C.E.P.R. Discussion Papers.
    8. Carlo A. Favero & Alessandro Melone, 2019. "Asset Pricing vs Asset Expected Returning in Factor Models," Working Papers 651, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2020. "Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors," Econometrics, MDPI, vol. 8(1), pages 1-23, February.
    10. Stoupos, Nikolaos & Nikas, Christos & Kiohos, Apostolos, 2023. "Turkey: From a thriving economic past towards a rugged future? - An empirical analysis on the Turkish financial markets," Emerging Markets Review, Elsevier, vol. 54(C).

  47. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2014. "No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," CEPR Discussion Papers 9848, C.E.P.R. Discussion Papers.

    Cited by:

    1. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    2. Huber, Florian & Krisztin, Tamás & Piribauer, Philipp, 2014. "Forecasting Global Equity Indices Using Large Bayesian VARs," Department of Economics Working Paper Series 184, WU Vienna University of Economics and Business.
    3. Andrea Renzetti, 2023. "Theory coherent shrinkage of Time-Varying Parameters in VARs," Papers 2311.11858, arXiv.org, revised Nov 2024.
    4. Gregor Bäurle & Daniel Kaufmann, 2018. "Measuring Exchange Rate, Price, and Output Dynamics at the Effective Lower Bound," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(6), pages 1243-1266, December.

  48. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2014. "Have standard VARs remained stable since the crisis?," Working Paper 2014/13, Norges Bank.

    Cited by:

    1. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    2. Anastasios Evgenidis & Stephanos Papadamou, 2021. "The impact of unconventional monetary policy in the euro area. Structural and scenario analysis from a Bayesian VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5684-5703, October.
    3. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    4. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2021. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Papers 2112.01995, arXiv.org, revised Nov 2022.
    5. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    6. Hacioglu Hoke, Sinem, 2019. "Macroeconomic effects of political risk shocks," Bank of England working papers 841, Bank of England.
    7. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Paper 2019/2, Norges Bank.
    8. Orkideh Gharehgozli & Sunhyung Lee, 2022. "Money Supply and Inflation after COVID-19," Economies, MDPI, vol. 10(5), pages 1-14, April.
    9. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    10. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    11. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    12. Rachidi Kotchoni & Dalibor Stevanovic, 2020. "GDP Forecast Accuracy During Recessions," Working Papers 20-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    13. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    14. Rachidi Kotchoni & Dalibor Stevanovic, 2016. "Forecasting U.S. Recessions and Economic Activity," Working Papers hal-04141569, HAL.
    15. Karlsson, Sune & Österholm, Pär, 2018. "A Note on the Stability of the Swedish Philips Curve," Working Papers 2018:6, Örebro University, School of Business.
    16. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    17. Jarociński, Marek & Bobeica, Elena, 2017. "Missing disinflation and missing inflation: the puzzles that aren't," Working Paper Series 2000, European Central Bank.
    18. Kavanagh, Ella & Zhu, Sheng & O’Sullivan, Niall, 2022. "Monetary policy, trade-offs and the transmission of UK Monetary Policy," Journal of Policy Modeling, Elsevier, vol. 44(6), pages 1128-1147.
    19. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    20. Antonio Maria Conti & Stefano Neri & Alessandro Notarpietro, 2024. "Credit strikes back: the macroeconomic impact of the 2022-23 ECB monetary tightening and the role of lending rates," Questioni di Economia e Finanza (Occasional Papers) 884, Bank of Italy, Economic Research and International Relations Area.
    21. Petrella, Ivan & Antolin-Diaz, Juan & Rubio-Ramírez, Juan Francisco, 2018. "Structural Scenario Analysis with SVARs," CEPR Discussion Papers 12579, C.E.P.R. Discussion Papers.
    22. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers 2014-25, Federal Reserve Bank of St. Louis.
    23. Aymeric Ortmans, 2020. "Evolving Monetary Policy in the Aftermath of the Great Recession," Documents de recherche 20-01, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    24. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
    25. Shang, Fei, 2022. "The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    26. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-switching three-pass regression filter," Working Papers 1748, Banco de España.
    27. Conti, Antonio M. & Nobili, Andrea & Signoretti, Federico M., 2023. "Bank capital requirement shocks: A narrative perspective," European Economic Review, Elsevier, vol. 151(C).
    28. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.
    29. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    30. Conti, Antonio M., 2021. "Resurrecting the Phillips Curve in Low-Inflation Times," Economic Modelling, Elsevier, vol. 96(C), pages 172-195.
    31. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
    32. Karlsson, Sune & Österholm, Pär, 2019. "Volatilities, drifts and the relation between treasury yields and the corporate bond yield spread in australia," Finance Research Letters, Elsevier, vol. 30(C), pages 378-384.
    33. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    34. Edvinsson, Rodney & Karlsson, Sune & Österholm, Pär, 2023. "Does Money Growth Predict Inflation? Evidence from Vector Autoregressions Using Four Centuries of Data," Working Papers 2023:3, Örebro University, School of Business.
    35. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    36. Benjamin Garcia & Arsenios Skaperdas, 2017. "Inferring the Shadow Rate from Real Activity," Finance and Economics Discussion Series 2017-106, Board of Governors of the Federal Reserve System (U.S.).
    37. Neville Francis & Laura E. Jackson & Michael T. Owyang, 2014. "How Has Empirical Monetary Policy Analysis Changed After the Financial Crisis?," Working Papers 2014-19, Federal Reserve Bank of St. Louis.

  49. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.

    Cited by:

    1. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    2. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    3. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    4. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Economic Modelling, Elsevier, vol. 120(C).
    5. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    6. Jian Chai & Puju Cao & Xiaoyang Zhou & Kin Keung Lai & Xiaofeng Chen & Siping (Sue) Su, 2018. "The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data," Energies, MDPI, vol. 11(6), pages 1-14, May.
    7. Arabinda Basistha, "undated". "Estimates of Quarterly and Monthly Episodes of Global Recessions: Evidence from Markov-switching Dynamic Factor Models," Working Papers 24-07, Department of Economics, West Virginia University.
    8. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    9. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    10. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    11. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, May.
    12. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    13. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    14. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    15. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    16. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.

  50. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.

    Cited by:

    1. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    2. Laurent Ferrara & Pierre Guérin, 2015. "What Are The Macroeconomic Effects of High-Frequency Uncertainty Shocks?," EconomiX Working Papers 2015-12, University of Paris Nanterre, EconomiX.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    4. Skrobotov, Anton (Скроботов, Антон) & Turuntseva, Marina (Турунцева, Марина), 2015. "Theoretical Aspects of Modeling of the SVAR [Теоретические Аспекты Моделирования Svar]," Published Papers mak8, Russian Presidential Academy of National Economy and Public Administration.
    5. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    6. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.

  51. Claudia Foroni & Massimiliano Marcellino, 2013. "Mixed frequency structural models: estimation, and policy analysis," Working Paper 2013/15, Norges Bank.

    Cited by:

    1. Kristina Bluwstein & Fabio Canova, 2016. "Beggar-Thy-Neighbor? The International Effects of ECB Unconventional Monetary Policy Measures," International Journal of Central Banking, International Journal of Central Banking, vol. 12(3), pages 69-120, September.
    2. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    3. Giannone, Domenico & Monti, Francesca & Reichlin, Lucrezia, 2014. "Exploiting the monthly data-flow in structural forecasting," LSE Research Online Documents on Economics 57998, London School of Economics and Political Science, LSE Library.
    4. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.

  52. Yuliya Rychalovska & Massimiliano Marcellino (EUI), 2013. "An estimated DSGE model of a Small Open Economy within the Monetary Union: Forecasting and Structural Analysis," EcoMod2013 5302, EcoMod.

    Cited by:

    1. Bettendorf, Timo, 2013. "Feeding the Global VAR with theory: Is German wage moderation to blame for European imbalances?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79710, Verein für Socialpolitik / German Economic Association.
    2. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.

  53. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2013. "Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility," Temi di discussione (Economic working papers) 896, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    2. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    3. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    4. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    5. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    6. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Measuring Uncertainty and Its Impact on the Economy," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 799-815, December.
    8. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    9. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    11. Krist'of N'emeth & D'aniel Hadh'azi, 2024. "Generating density nowcasts for U.S. GDP growth with deep learning: Bayes by Backprop and Monte Carlo dropout," Papers 2405.15579, arXiv.org.
    12. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    13. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    14. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    15. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    16. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    17. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    18. Antolín-Díaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2024. "Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails," Journal of Econometrics, Elsevier, vol. 238(2).
    19. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    20. Vegard H. Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Paper 2018/3, Norges Bank.
    21. Ana Arencibia Pareja & Ana Gomez-Loscos & Mercedes de Luis López & Gabriel Perez-Quiros, 2020. "A Short Term Forecasting Model for the Spanish GDP and itsDemand Components," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 43(85), pages 1-30.
    22. Guido Bulligan & Fabio Busetti & Michele Caivano & Pietro Cova & Davide Fantino & Alberto Locarno & Lisa Rodano, 2017. "The Bank of Italy econometric model: an update of the main equations and model elasticities," Temi di discussione (Economic working papers) 1130, Bank of Italy, Economic Research and International Relations Area.
    23. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    24. Leif Anders Thorsrud, 2016. "Words are the new numbers: A newsy coincident index of business cycles," Working Papers No 4/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    25. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    26. Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2017. "Tracking the slowdown in long-run GDP growth," LSE Research Online Documents on Economics 81869, London School of Economics and Political Science, LSE Library.
    27. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2022. "Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications," Papers 2211.16121, arXiv.org, revised Aug 2024.
    28. Antonello D’Agostino & Jacopo Cimadomo, 2015. "Combining time-variation and mixed-frequencies: an analysis of government spending multipliers in Italy," Working Papers 7, European Stability Mechanism.
    29. Sebastian Ankargren & Paulina Jon'eus, 2019. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Papers 1907.01075, arXiv.org.
    30. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
    31. Ahiadorme, Johnson Worlanyo, 2020. "Monetary policy transmission and income inequality in Sub-Saharan Africa," MPRA Paper 104084, University Library of Munich, Germany.
    32. Petrella, Ivan & Drechsel, Thomas & Antolin-Diaz, Juan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
    33. Liu, Yang & Swanson, Norman R., 2024. "An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1391-1409.
    34. Fumio Hayashi & Yuta Tachi, 2023. "Nowcasting Japan’s GDP," Empirical Economics, Springer, vol. 64(4), pages 1699-1735, April.
    35. Hauber, Philipp, 2022. "Real-time nowcasting with sparse factor models," EconStor Preprints 251551, ZBW - Leibniz Information Centre for Economics.
    36. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    37. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    38. Leif Anders Thorsrud, 2016. "Nowcasting using news topics. Big Data versus big bank," Working Paper 2016/20, Norges Bank.
    39. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    40. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    41. Cem Cakmakli & Hamza Demircan, 2020. "Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic," Koç University-TUSIAD Economic Research Forum Working Papers 2016, Koc University-TUSIAD Economic Research Forum.
    42. Boriss Siliverstovs, 2020. "Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts," Empirical Economics, Springer, vol. 58(1), pages 7-27, January.
    43. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2016. "Common Faith or Parting Ways? A Time Varying Parameters Factor Analysis of Euro-Area Inflation," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 539-565, Emerald Group Publishing Limited.
    44. Manu García & Juan F. Rubio-Ramírez, 2019. "Now-casting Spain," Working Papers 2019-03, FEDEA.
    45. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
    46. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    47. Robert Lehmann & Magnus Reif & Timo Wollmershäuser, 2020. "ifoCAST: Der neue Prognosestandard des ifo Instituts," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 73(11), pages 31-39, November.
    48. Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Potjagailo, Galina, 2018. "Deutsche Konjunktur im Frühjahr 2018 - Deutsche Wirtschaft näher am Limit [German Economy Spring 2018 - German economy closer to its limit]," Kieler Konjunkturberichte 41, Kiel Institute for the World Economy (IfW Kiel).
    49. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    50. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.

  54. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.

    Cited by:

    1. Jaime Martínez-Martín & Elena Rusticelli, 2020. "Keeping track of global trade in real time," Working Papers 2019, Banco de España.
    2. Paul Labonne & Martin Weale, 2020. "Temporal disaggregation of overlapping noisy quarterly data: estimation of monthly output from UK value‐added tax data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1211-1230, June.
    3. Katja Drechsel & Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    4. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    5. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
    6. Joan Paredes & Javier J. Pérez & Gabriel Perez Quiros, 2023. "Fiscal targets. A guide to forecasters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 472-492, June.
    7. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    8. Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
    9. Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.

  55. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2013. "Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility," CEPR Discussion Papers 9312, C.E.P.R. Discussion Papers.

    Cited by:

    1. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.
    2. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    3. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    4. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    5. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    6. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    7. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting," Papers 2404.02671, arXiv.org, revised Nov 2024.
    8. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    9. Edward S. Knotek & Saeed Zaman, 2014. "Nowcasting U.S. Headline and Core Inflation," Working Papers (Old Series) 1403, Federal Reserve Bank of Cleveland.
    10. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    11. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    12. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    13. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2021. "Measuring the effectiveness of US monetary policy during the COVID‐19 recession," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(3), pages 287-297, July.
    14. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    15. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    16. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
    17. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    18. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    19. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    20. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.
    21. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    22. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    23. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    24. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    25. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Economic Modelling, Elsevier, vol. 120(C).
    26. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    27. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    28. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    29. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    30. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    31. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    32. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    33. Edward S. Knotek & Saeed Zaman, 2020. "Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach," Working Papers 20-31, Federal Reserve Bank of Cleveland.
    34. Antonello D’Agostino & Jacopo Cimadomo, 2015. "Combining time-variation and mixed-frequencies: an analysis of government spending multipliers in Italy," Working Papers 7, European Stability Mechanism.
    35. Jos Jansen, W. & Jin, Xiaowen & Winter, Jasper M. de, 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," Munich Reprints in Economics 43488, University of Munich, Department of Economics.
    36. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    37. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    38. Boriss Siliverstovs, 2021. "New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?," Econometrics, MDPI, vol. 9(1), pages 1-25, March.
    39. Liu, Yang & Swanson, Norman R., 2024. "An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1391-1409.
    40. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    41. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    42. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, May.
    43. Deborah Gefang & Gary Koop & Aubrey Poon, 2020. "Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-07, Economic Statistics Centre of Excellence (ESCoE).
    44. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2017. "Density Forecasts With Midas Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 783-801, June.
    45. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    46. Robert M. Kunst & Martin Wagner, 2020. "Economic forecasting: editors’ introduction," Empirical Economics, Springer, vol. 58(1), pages 1-5, January.
    47. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    48. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    49. Cem Cakmakli & Hamza Demircan, 2020. "Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic," Koç University-TUSIAD Economic Research Forum Working Papers 2016, Koc University-TUSIAD Economic Research Forum.
    50. Boriss Siliverstovs, 2020. "Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts," Empirical Economics, Springer, vol. 58(1), pages 7-27, January.
    51. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    52. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    53. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    54. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    55. Lucas P. Harlaar & Jacques J.F. Commandeur & Jan A. van den Brakel & Siem Jan Koopman & Niels Bos & Frits D. Bijleveld, 2024. "Statistical Early Warning Models with Applications," Tinbergen Institute Discussion Papers 24-037/III, Tinbergen Institute.
    56. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
    57. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
    58. Boriss Siliverstovs, 2021. "Gauging the Effect of Influential Observations on Measures of Relative Forecast Accuracy in a Post-COVID-19 Era: Application to Nowcasting Euro Area GDP Growth," Working Papers 2021/01, Latvijas Banka.
    59. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    60. Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022. "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers 964, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    61. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    62. González-Astudillo, Manuel & Baquero, Daniel, 2019. "A nowcasting model for Ecuador: Implementing a time-varying mean output growth," Economic Modelling, Elsevier, vol. 82(C), pages 250-263.
    63. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    64. Dario Caldara & Chiara Scotti & Molin Zhong, 2021. "Macroeconomic and Financial Risks: A Tale of Mean and Volatility," International Finance Discussion Papers 1326, Board of Governors of the Federal Reserve System (U.S.).

  56. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.

    Cited by:

    1. Aliocha Accardo & Sylv rie Herbert & Cristina Jude & Adrian Penalver, 2023. "Measuring and Comparing Consumption Inequality between France and the United States," Working papers 904, Banque de France.
    2. Afees A. Salisu & Rangan Gupta, 2019. "How do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch," Working Papers 201946, University of Pretoria, Department of Economics.
    3. Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
    4. Chudik, Alexander & Georgiadis, Georgios, 2019. "Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables," Working Paper Series 2307, European Central Bank.
    5. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
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    38. Sylvia Kaufmann, 2023. "Covid-19 outbreak and beyond: a retrospect on the information content of short-time workers for GDP now- and forecasting," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-10, December.
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    40. Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022. "Forecasting oil prices: New approaches," Energy, Elsevier, vol. 238(PC).
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    44. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
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    49. Grant Allan & Gary Koop & Stuart McIntyre & Paul Smith, 2019. "Nowcasting Using Mixed Frequency Methods: An Application to the Scottish Economy," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 12-45, September.
    50. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
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  57. Marcellino, Massimiliano & Eickmeier, Sandra & Prieto, Esteban, 2013. "Time Variation in Macro-Financial Linkages," CEPR Discussion Papers 9436, C.E.P.R. Discussion Papers.

    Cited by:

    1. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2019. "Money, Credit, Monetary Policy, and the Business Cycle in the Euro Area: What Has Changed Since the Crisis?," International Journal of Central Banking, International Journal of Central Banking, vol. 15(5), pages 137-173, December.
    2. Angela Abbate & Sandra Eickmeier & Esteban Prieto, 2020. "Financial shocks and inflation dynamics," Working Papers 2020-13, Swiss National Bank.
    3. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    4. Andrea Silvestrini & Andrea Zaghini, 2015. "Financial Shocks And The Real Economy In A Nonlinear World: From Theory To Estimation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 15/910, Ghent University, Faculty of Economics and Business Administration.
    5. Angela Abbate & Massimiliano Marcellino, 2017. "Macroeconomic activity and risk indicators: an unstable relationship," BAFFI CAREFIN Working Papers 1756, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    6. Salzmann, Leonard, 2020. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224588, Verein für Socialpolitik / German Economic Association.
    7. Cornand, Camille & Gandré, Pauline & Gimet, Céline, 2016. "Increase in home bias in the Eurozone debt crisis: The role of domestic shocks," Economic Modelling, Elsevier, vol. 53(C), pages 445-469.
    8. Andrea Silvestrini & Andrea Zaghini, 2015. "Financial shocks and the real economy in a nonlinear world: a survey of the theoretical and empirical literature," Questioni di Economia e Finanza (Occasional Papers) 255, Bank of Italy, Economic Research and International Relations Area.
    9. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    10. Cheng, Chak Hung Jack & Chiu, Ching-Wai (Jeremy), 2016. "Nonlinearities of mortgage spreads over the business cycles," Bank of England working papers 634, Bank of England.
    11. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    12. Ellington, Michael, 2018. "Financial market illiquidity shocks and macroeconomic dynamics: Evidence from the UK," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 225-236.
    13. Camille Cornand & Pauline Gandré, 2013. "Home bias and self-fulfilling sovereign debt crisis," Post-Print halshs-00861603, HAL.
    14. Karlsson, Sune & Österholm, Pär, 2019. "The Relation between the Corporate Bond-Yield Spread and the Real Economy: Stable or TimeVarying?," Working Papers 2019:7, Örebro University, School of Business.
    15. Hilberg, Björn & Grill, Michael & Metiu, Norbert, 2016. "Credit constraints and the international propagation of US financial shocks," Working Paper Series 1954, European Central Bank.
    16. Carrillo Julio A. & García Ana Laura, 2021. "The COVID-19 Economic Crisis in Mexico through the Lens of a Financial Conditions Index," Working Papers 2021-23, Banco de México.
    17. Davidson, Sharada Nia & Moccero, Diego Nicolas, 2024. "The nonlinear effects of banks’ vulnerability to capital depletion in euro area countries," Working Paper Series 2912, European Central Bank.
    18. Knut Are Aastveit & Francesco Furlanetto & Francesca Loria, 2017. "Has the Fed responded to house and stock prices? A time-varying analysis," Working Papers 1713, Banco de España.
    19. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying volatility, financial intermediation and monetary policy," Discussion Papers 46/2016, Deutsche Bundesbank.
    20. Francesco Furlanetto & Francesco Ravazzolo & Samad Sarferaz, 2014. "Identification of financial factors in economic fluctuations," KOF Working papers 14-364, KOF Swiss Economic Institute, ETH Zurich.
    21. Antonio M. Conti & Stefano Neri & Andrea Nobili, 2015. "Why is inflation so low in the euro area?," Temi di discussione (Economic working papers) 1019, Bank of Italy, Economic Research and International Relations Area.
    22. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2018. "Bank capital constraints, lending supply and economic activity," Temi di discussione (Economic working papers) 1199, Bank of Italy, Economic Research and International Relations Area.
    23. Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2022. "The Relation between the High-Yield Bond Spread and the Unemployment Rate in the Euro Area," Finance Research Letters, Elsevier, vol. 46(PA).
    24. Cotter, John & Hallam, Mark & Yilmaz, Kamil, 2023. "Macro-financial spillovers," Journal of International Money and Finance, Elsevier, vol. 133(C).
    25. Francesco Corsello & Valerio Nispi Landi, 2020. "Labor Market and Financial Shocks: A Time‐Varying Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 777-801, June.
    26. Marcus Ingholt, 2018. "LTV vs. DTI Constraints: When Did They Bind, and How Do They Interact?," 2018 Meeting Papers 866, Society for Economic Dynamics.
    27. Hosszú, Zsuzsanna, 2018. "The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach," Economic Systems, Elsevier, vol. 42(1), pages 32-44.
    28. Conti, Antonio M. & Nobili, Andrea & Signoretti, Federico M., 2023. "Bank capital requirement shocks: A narrative perspective," European Economic Review, Elsevier, vol. 151(C).
    29. Karlsson, Sune & Österholm, Pär, 2020. "A hybrid time-varying parameter Bayesian VAR analysis of Okun’s law in the United States," Economics Letters, Elsevier, vol. 197(C).
    30. Aurelien Leroy & Adrian Pop, 2019. "Macro-Financial Linkages: The Role of the Institutional Framework," Post-Print hal-03367548, HAL.
    31. Neri, Stefano & Nobili, Andrea & Conti, Antonio M., 2017. "Low inflation and monetary policy in the euro area," Working Paper Series 2005, European Central Bank.
    32. Muhammad Zeshan & Wasim Shahid Malik & Muhammad Nasir, 2019. "Oil Price Shocks, Systematic Monetary Policy and Economic Activity," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 58(1), pages 65-81.
    33. Narcissa Balta & Bořek Vašíček, 2020. "Financial channels and economic activity in the euro area: a large-scale Bayesian VAR approach," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(2), pages 431-451, May.
    34. Giovanni Dell'Ariccia & Karl Habermeier & Vikram Haksar & Tommaso Mancini-Griffoli, 2017. "Monetary Policy and Financial Stability," RBA Annual Conference Volume (Discontinued), in: Jonathan Hambur & John Simon (ed.),Monetary Policy and Financial Stability in a World of Low Interest Rates, Reserve Bank of Australia.
    35. Liu, Tangyong & Gong, Xu, 2020. "Analyzing time-varying volatility spillovers between the crude oil markets using a new method," Energy Economics, Elsevier, vol. 87(C).
    36. Kiss, Tamás & Österholm, Pär, 2020. "Fat tails in leading indicators," Economics Letters, Elsevier, vol. 193(C).
    37. Haroon Mumtaz & Konstantinos Theodoridis, 2016. "Volatility Co-movement and the Great Moderation. An Empirical Analysis," Working Papers 804, Queen Mary University of London, School of Economics and Finance.
    38. Bijsterbosch, Martin & Falagiarda, Matteo, 2015. "The macroeconomic impact of financial fragmentation in the euro area: Which role for credit supply?," Journal of International Money and Finance, Elsevier, vol. 54(C), pages 93-115.
    39. Baumann, Ursel & Lodge, David & Miescu, Mirela S., 2019. "Global growth on life support? The contributions of fiscal and monetary policy since the global financial crisis," Working Paper Series 2248, European Central Bank.
    40. Zhang, Wen, 2019. "Deciphering the causes for the post-1990 slow output recoveries," Economics Letters, Elsevier, vol. 176(C), pages 28-34.
    41. Paolo Gorgi & Siem Jan Koopman & Julia Schaumburg, 2021. "Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors," Tinbergen Institute Discussion Papers 21-056/III, Tinbergen Institute.
    42. Salzmann, Leonard, 2019. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," EconStor Preprints 206691, ZBW - Leibniz Information Centre for Economics.
    43. Michael Ellington & Chris Florackis & Costas Milas, 2016. "Liquidity Shocks and Real GDP Growth: Evidence from a Bayesian Time-varying Parameter VAR," Working Paper series 16-28, Rimini Centre for Economic Analysis.
    44. Martin Harding & Rafael Wouters, 2022. "Risk and State-Dependent Financial Frictions," Staff Working Papers 22-37, Bank of Canada.

  58. Eric Ghysels & Pierre Guérin & Massimiliano Marcellino, 2013. "Regime Switches in the Risk-Return Trade-Off," Staff Working Papers 13-51, Bank of Canada.

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    1. Chotipong Charoensom, 2024. "An Estimation of Regime Switching Models with Nonlinear Endogenous Switching," PIER Discussion Papers 217, Puey Ungphakorn Institute for Economic Research.
    2. Aragó, V. & Barreda-Tarrazona, I. & Breaban, A. & Matallín, J.C. & Salvador, E., 2022. "Market risk aversion under volatility shifts: An experimental study," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 552-568.
    3. Suzanne G. M. Fifield & David G. McMillan & Fiona J. McMillan, 2020. "Is there a risk and return relation?," The European Journal of Finance, Taylor & Francis Journals, vol. 26(11), pages 1075-1101, July.
    4. Frazier, David T. & Liu, Xiaochun, 2016. "A new approach to risk-return trade-off dynamics via decomposition," Journal of Economic Dynamics and Control, Elsevier, vol. 62(C), pages 43-55.
    5. Naqi Shah, Sadia & Qayyum, Abdul, 2016. "Analyse Risk-Return Paradox: Evidence from Electricity Sector of Pakistan," MPRA Paper 68783, University Library of Munich, Germany.
    6. Licheng Sun & Liang Meng & Mohammad Najand, 2017. "The Role of U.S. Market on International Risk-Return Tradeoff Relations," The Financial Review, Eastern Finance Association, vol. 52(3), pages 499-526, August.
    7. Jorge M. Uribe, 2018. "“Scaling Down Downside Risk with Inter-Quantile Semivariances”," IREA Working Papers 201826, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    8. Amanjot Singh & Parneet Kaur, 2017. "Does US Financial Stress Explain Risk–Return Dynamics in Indian Equity Market? A Logistic Regression Approach," Vision, , vol. 21(1), pages 13-22, March.
    9. Nektarios Aslanidis & Charlotte Christiansen, 2017. "Flight to Safety from European Stock Markets," CREATES Research Papers 2017-38, Department of Economics and Business Economics, Aarhus University.
    10. Cotter, John & Salvador, Enrique, 2022. "The non-linear trade-off between return and risk and its determinants," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 100-132.
    11. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun & Wang, Weining, 2021. "Long- and short-run components of factor betas: Implications for stock pricing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    12. Petar Sabtchevsky & Paul Whelan & Andrea Vedolin & Philippe Mueller, 2017. "Variance Risk Premia on Stocks and Bonds," 2017 Meeting Papers 1161, Society for Economic Dynamics.
    13. Yun Xie & Yixiang Tian & Zhuang Xiao & Xiangyun Zhou, 2018. "Dependence of credit spread and macro-conditions based on an alterable structure model," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.
    14. Chelikani, Surya & Marks, Joseph M. & Nam, Kiseok, 2024. "State-dependent intertemporal risk-return tradeoff: Further evidence," Journal of Economics and Business, Elsevier, vol. 130(C).
    15. Jyri Kinnunen & Minna Martikainen, 2017. "Dynamic Autocorrelation and International Portfolio Allocation," Multinational Finance Journal, Multinational Finance Journal, vol. 21(1), pages 21-48, March.
    16. Salvador, Enrique & Floros, Christos & Arago, Vicent, 2014. "Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 60-77.
    17. Miguel A. Duran, 2024. "The Risk-Return Relation in the Corporate Loan Market," Papers 2401.12315, arXiv.org.
    18. Tobias Adrian & Richard K. Crump & Erik Vogt, 2019. "Nonlinearity and Flight‐to‐Safety in the Risk‐Return Trade‐Off for Stocks and Bonds," Journal of Finance, American Finance Association, vol. 74(4), pages 1931-1973, August.
    19. Kinnunen, Jyri & Martikainen, Minna, 2015. "Expected returns and idiosyncratic risk: Industry-level evidence from Russia," BOFIT Discussion Papers 30/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    20. Liu, Xiaochun, 2017. "Unfolded risk-return trade-offs and links to Macroeconomic Dynamics," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 1-19.
    21. Ahmed, Walid M.A., 2020. "Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin," Journal of Economics and Business, Elsevier, vol. 108(C).
    22. Rachidi Kotchoni, 2018. "Detecting and Measuring Nonlinearity," Post-Print hal-02435765, HAL.
    23. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou & Weining Wang, 2017. "Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing," CREATES Research Papers 2017-34, Department of Economics and Business Economics, Aarhus University.
    24. Byrne, Joseph P. & Sakemoto, Ryuta, 2021. "The conditional volatility premium on currency portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    25. Joseph, Byrne & Sakemoto, Ryuta, 2020. "The Conditional Risk and Return Trade-Off on Currency Portfolios," MPRA Paper 99497, University Library of Munich, Germany.
    26. Amanjot Singh & Manjit Singh, 2016. "Risk–Return Relationship in BRIC Equity Markets: Evidence from Markov Regime Switching Model with Time-varying Transition Probabilities," Metamorphosis: A Journal of Management Research, , vol. 15(2), pages 69-78, December.
    27. Liu, Xiaochun, 2017. "Can macroeconomic dynamics explain the time variation of risk–return trade-offs in the U.S. financial market?," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 275-293.
    28. Nektarios Aslanidis & Charlotte Christiansen & Christos S. Savva, 2013. "Risk-Return Trade-Off for European Stock Markets," CREATES Research Papers 2013-31, Department of Economics and Business Economics, Aarhus University.
    29. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
    30. Aslanidis, Nektarios & Christiansen, Charlotte & Savva, Christos S., 2020. "Flight-to-safety and the risk-return trade-off: European evidence," Finance Research Letters, Elsevier, vol. 35(C).
    31. Huang, Qiubin & de Haan, Jakob & Scholtens, Bert, 2020. "Does bank capitalization matter for bank stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

  59. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.

    Cited by:

    1. Schumacher, Christian & Marcellino, Massimiliano & Foroni, Claudia, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.
    2. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    3. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    4. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    5. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    6. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    7. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    8. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    9. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.

  60. Òscar Jordà & Malte Knuppel & Massimiliano Marcellino, 2012. "Empirical simultaneous prediction regions for path-forecasts," Working Paper Series 2012-05, Federal Reserve Bank of San Francisco.

    Cited by:

    1. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
    2. Tara Sinclair & Herman O. Stekler & Warren Carrow, 2012. "Evaluating a Vector of the Fed's Forecasts," Working Papers 2012-3, The George Washington University, Institute for International Economic Policy.
    3. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    4. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    5. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    6. Tara Sinclair & Herman O. Stekler & Warren Carnow, 2012. "A New Approach For Evaluating Economic Forecasts," Working Papers 2012-2, The George Washington University, Institute for International Economic Policy.
    7. Svetlana Makarova, 2014. "Risk and Uncertainty: Macroeconomic Perspective," UCL SSEES Economics and Business working paper series 129, UCL School of Slavonic and East European Studies (SSEES).
    8. Hans Christian Müller-Dröge & Tara M. Sinclair & Herman O. Stekler, 2014. "Evaluating Forecasts Of A Vector Of Variables: A German Forecasting Competition," Working Papers 2014-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    10. Ilhan Kilic & Faruk Balli, 2024. "Measuring economic country-specific uncertainty in Türkiye," Empirical Economics, Springer, vol. 67(4), pages 1649-1689, October.
    11. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    12. Thomai Filippeli, 2011. "Theoretical Priors for BVAR Models & Quasi-Bayesian DSGE Model Estimation," 2011 Meeting Papers 396, Society for Economic Dynamics.
    13. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
    14. Filippeli, Thomai & Harrison, Richard & Theodoridis, Konstantinos, 2018. "DSGE-based Priors for BVARs & Quasi-Bayesian DSGE Estimation," Cardiff Economics Working Papers E2018/5, Cardiff University, Cardiff Business School, Economics Section.
    15. Knüppel, Malte, 2018. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," International Journal of Forecasting, Elsevier, vol. 34(1), pages 105-116.
    16. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    17. Michael Wolf & Dan Wunderli, 2012. "Bootstrap joint prediction regions," ECON - Working Papers 064, Department of Economics - University of Zurich, revised May 2013.
    18. Filippeli, Thomai & Harrison, Richard & Theodoridis, Konstantinos, 2020. "DSGE-based priors for BVARs and quasi-Bayesian DSGE estimation," Econometrics and Statistics, Elsevier, vol. 16(C), pages 1-27.
    19. Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Tara M. Sinclair & H.O. Stekler, 2011. "Differences in Early GDP Component Estimates Between Recession and Expansion," Working Papers 2011-05, The George Washington University, Institute for International Economic Policy.

  61. Guido Bulligan & Massimiliano Marcellino & Fabrizio Venditti, 2012. "Forecasting economic activity with higher frequency targeted predictors," Temi di discussione (Economic working papers) 847, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Marie Bessec, 2013. "Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 500-511, September.
    2. Matteo Luciani & Libero Monteforte, 2012. "Uncertainty and Heterogeneity in factor models forecasting," Working Papers 5, Department of the Treasury, Ministry of the Economy and of Finance.
    3. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    4. Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.
    5. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    6. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
    7. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    8. Aaron G. Grech, "undated". "The European Commission’s business and consumer surveys and Maltese macroeconomic trends," CBM Policy Papers PP/05/2019, Central Bank of Malta.
    9. Christophe Piette, 2016. "Predicting Belgium’s GDP using targeted bridge models," Working Paper Research 290, National Bank of Belgium.
    10. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    11. Germán López, 2015. "Forecast Accuracy of Small and Large Scale Dynamic Factor Models in Developing Economies," Working Papers. Serie AD 2015-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    12. Aaron G. Grech & Reuben Ellul, 2021. "Are the European Commission’s Business and Consumer Survey Results Coincident Indicators for Maltese Economic Activity?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 91-108, April.
    13. Ch. Piette & G. Langenus, 2014. "Using BREL to nowcast the Belgian business cycle: the role of survey data," Economic Review, National Bank of Belgium, issue i, pages 75-98, June.
    14. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  62. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2012. "Selecting predictors by using Bayesian model averaging in bridge models," Temi di discussione (Economic working papers) 872, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    3. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    5. Guido Bulligan & Fabio Busetti & Michele Caivano & Pietro Cova & Davide Fantino & Alberto Locarno & Lisa Rodano, 2017. "The Bank of Italy econometric model: an update of the main equations and model elasticities," Temi di discussione (Economic working papers) 1130, Bank of Italy, Economic Research and International Relations Area.
    6. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    7. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    8. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).

  63. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2012. "Common Drifting Volatility in Large Bayesian VARs," CEPR Discussion Papers 8894, C.E.P.R. Discussion Papers.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    3. Jonas Dovern & Martin Feldkircher & Florian Huber, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR," Working Papers 200, Oesterreichische Nationalbank (Austrian Central Bank).
    4. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    5. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    6. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    7. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2020. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 934-943, September.
    8. Ching-Wai Chiu & Haroon Mumtaz & Gabor Pinter, 2016. "VAR Models with Non-Gaussian Shocks," Discussion Papers 1609, Centre for Macroeconomics (CFM).
    9. MeiChi Huang, 2022. "Time‐varying impacts of expectations on housing markets across hot and cold phases," International Finance, Wiley Blackwell, vol. 25(2), pages 249-265, August.
    10. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    11. Joshua C. C. Chan & Gary Koop & Xuewen Yu, 2024. "Large Order-Invariant Bayesian VARs with Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 825-837, April.
    12. Gary Koop & Stuart McIntyre & James Mitchell, 2018. "UK Regional Nowcasting using a Mixed Frequency Vector Autoregressive Model," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-07, Economic Statistics Centre of Excellence (ESCoE).
    13. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    14. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    15. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    16. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    17. Uribe Jorge M. & Chuliá Helena, 2023. "Expected, unexpected, good and bad aggregate uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 265-284, April.
    18. Jan Philipp Fritsche & Mathias Klein & Malte Rieth, 2020. "Government Spending Multipliers in (Un)certain Times," Discussion Papers of DIW Berlin 1901, DIW Berlin, German Institute for Economic Research.
    19. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    20. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    21. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    22. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    23. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage," CAMA Working Papers 2019-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    24. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    25. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    26. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    27. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    28. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2021. "Measuring the effectiveness of US monetary policy during the COVID‐19 recession," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(3), pages 287-297, July.
    29. Michael Pfarrhofer, 2024. "Forecasts with Bayesian vector autoregressions under real time conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 771-801, April.
    30. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial regimes and uncertainty shocks," BCAM Working Papers 1404, Birkbeck Centre for Applied Macroeconomics.
    31. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    32. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    33. Koop, Gary & Korobilis, Dimitris, 2012. "Large time-varying parameter VARs," MPRA Paper 38591, University Library of Munich, Germany.
    34. Simon Beyeler, 2019. "Streamlining Time-varying VAR with a Factor Structure in the Parameters," Working Papers 19.03, Swiss National Bank, Study Center Gerzensee.
    35. Valeriu Nalban & Andra Smadu, 2020. "Financial disruptions and heightened uncertainty: a case for timely policy action," Working Papers 687, DNB.
    36. Michele Lenza & Giorgio E. Primiceri, 2020. "How to Estimate a VAR after March 2020," NBER Working Papers 27771, National Bureau of Economic Research, Inc.
    37. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    38. Nalban, Valeriu & Smădu, Andra, 2021. "Asymmetric effects of uncertainty shocks: Normal times and financial disruptions are different," Journal of Macroeconomics, Elsevier, vol. 69(C).
    39. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    40. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    41. Florian Huber & Gary Koop, 2024. "Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1301-1320, November.
    42. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
    43. Knüppel, Malte & Krüger, Fabian, 2017. "Forecast Uncertainty, Disagreement, and Linear Pools of Density Forecasts," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168294, Verein für Socialpolitik / German Economic Association.
    44. Follett, Lendie & Yu, Cindy, 2019. "Achieving parsimony in Bayesian vector autoregressions with the horseshoe prior," Econometrics and Statistics, Elsevier, vol. 11(C), pages 130-144.
    45. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.
    46. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    47. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    48. Kelly Trinh & Bo Zhang & Chenghan Hou, 2025. "Macroeconomic real‐time forecasts of univariate models with flexible error structures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 59-78, January.
    49. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    50. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    51. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    52. Huber, Florian & Krisztin, Tamás & Piribauer, Philipp, 2014. "Forecasting Global Equity Indices Using Large Bayesian VARs," Department of Economics Working Paper Series 184, WU Vienna University of Economics and Business.
    53. Jamie L. Cross & Aubrey Poon, 2020. "On the contribution of international shocks in Australian business cycle fluctuations," Empirical Economics, Springer, vol. 59(6), pages 2613-2637, December.
    54. Frank C. Z. Wu, 2024. "Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(4), pages 697-704, June.
    55. Francisco Serranito & Nicolas Himounet & Julien Vauday, 2023. "Uncertainty is bad for Business. Really?," Working Papers hal-04219283, HAL.
    56. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    57. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    58. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    59. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2022. "Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications," Papers 2211.16121, arXiv.org, revised Aug 2024.
    60. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    61. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    62. Dovern, Jonas & Feldkircher, Martin & Huber, Florian, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Multivariate Forecasts from a Bayesian GVAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112999, Verein für Socialpolitik / German Economic Association.
    63. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    64. Sebastian Ankargren & Paulina Jon'eus, 2019. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Papers 1907.01075, arXiv.org.
    65. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Measuring uncertainty in the stock market," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 18-33.
    66. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2014. "No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," CEPR Discussion Papers 9848, C.E.P.R. Discussion Papers.
    67. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Large Vector Autoregressions with Asymmetric Priors," Working Papers 759, Queen Mary University of London, School of Economics and Finance.
    68. Florian Huber, 2014. "Density Forecasting using Bayesian Global Vector Autoregressions with Common Stochastic Volatility," Department of Economics Working Papers wuwp179, Vienna University of Economics and Business, Department of Economics.
    69. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    70. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    71. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    72. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    73. Haroon Mumtaz & Laura Sunder-Plassmann & Angeliki Theophilopoulou, 2016. "The State Level Impact of Uncertainty Shocks," Working Papers 793, Queen Mary University of London, School of Economics and Finance.
    74. Bognanni, Mark & Zito, John, 2020. "Sequential Bayesian inference for vector autoregressions with stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    75. Irina Zviadadze, 2014. "Term-structure of consumption risk premia in the cross-section of currency returns," 2014 Meeting Papers 1075, Society for Economic Dynamics.
    76. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    77. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    78. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
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    102. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    103. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    104. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
    105. Luo, Jiawen & Marfatia, Hardik A. & Ji, Qiang & Klein, Tony, 2023. "Co-volatility and asymmetric transmission of risks between the global oil and China's futures markets," Energy Economics, Elsevier, vol. 117(C).
    106. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2019. "Non-Gaussian VARMA model with stochastic volatility and applications in stock market bubbles," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 129-136.
    107. Christine Garnier & Elmar Mertens & Edward Nelson, 2015. "Trend Inflation in Advanced Economies," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 65-136, September.
    108. Haroon Mumtaz, 2020. "A Generalised Stochastic Volatility in Mean VAR. An Updated Algorithm," Working Papers 908, Queen Mary University of London, School of Economics and Finance.
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    110. Reifschneider, David & Tulip, Peter, 2019. "Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1564-1582.
    111. Cross, Jamie L. & Hou, Chenghan & Poon, Aubrey, 2020. "Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity," International Journal of Forecasting, Elsevier, vol. 36(3), pages 899-915.
    112. Haroon Mumtaz & Konstantinos Theodoridis, 2014. "The Changing Transmission of Uncertainty shocks in the US: An Empirical Analysis," Working Papers 735, Queen Mary University of London, School of Economics and Finance.
    113. Michele Lenza & Giorgio E. Primiceri, 2022. "How to estimate a vector autoregression after March 2020," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 688-699, June.
    114. Hadjiantoni, Stella & Kontoghiorghes, Erricos John, 2022. "An alternative numerical method for estimating large-scale time-varying parameter seemingly unrelated regressions models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 1-18.
    115. Andrea Carriero & Davide Pettenuzzo & Shubhranshu Shekhar, 2024. "Macroeconomic Forecasting with Large Language Models," Papers 2407.00890, arXiv.org, revised Mar 2025.
    116. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    117. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    118. Mark Bognanni & John Zito, 2019. "Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility," Working Papers 19-29, Federal Reserve Bank of Cleveland.
    119. Danilo Cascaldi-Garcia, 2022. "Pandemic Priors," International Finance Discussion Papers 1352, Board of Governors of the Federal Reserve System (U.S.).
    120. David L. Reifschneider & Peter Tulip, 2017. "Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors : The Federal Reserve's Approach," Finance and Economics Discussion Series 2017-020, Board of Governors of the Federal Reserve System (U.S.).
    121. Nam, Kyungsik, 2021. "Investigating the effect of climate uncertainty on global commodity markets," Energy Economics, Elsevier, vol. 96(C).
    122. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.
    123. Helena Chuliá & Jorge M. Uribe, 2019. "“Expected, Unexpected, Good and Bad Uncertainty"," IREA Working Papers 201919, University of Barcelona, Research Institute of Applied Economics, revised Nov 2019.
    124. Prüser, Jan, 2023. "Data-based priors for vector error correction models," International Journal of Forecasting, Elsevier, vol. 39(1), pages 209-227.
    125. Malte Knüppel & Fabian Krüger, 2022. "Forecast uncertainty, disagreement, and the linear pool," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 23-41, January.
    126. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 2012/09, Norges Bank.
    127. Crespo Cuaresma, Jesús & Huber, Florian & Onorante, Luca, 2020. "Fragility and the effect of international uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 108(C).
    128. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    129. Fu, Bowen, 2023. "Measuring the trend real interest rate in a data-rich environment," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    130. Aubrey Poon, 2018. "Assessing the Synchronicity and Nature of Australian State Business Cycles," The Economic Record, The Economic Society of Australia, vol. 94(307), pages 372-390, December.
    131. Paul Labonne, 2020. "Asymmetric uncertainty : Nowcasting using skewness in real-time data," Papers 2012.02601, arXiv.org, revised May 2024.
    132. Stefan Griller & Florian Huber & Michael Pfarrhofer, 2022. "Measuring Shocks to Central Bank Independence using Legal Rulings," Papers 2202.12695, arXiv.org.
    133. Hartwig, Benny, 2022. "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers 52/2022, Deutsche Bundesbank.
    134. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.
    135. James P. LeSage & Daniel Hendrikz, 2019. "Large Bayesian vector autoregressive forecasting for regions: A comparison of methods based on alternative disturbance structures," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 563-599, June.

  64. Szabolcs Deák & Lionel Fontagné & Marco Maffezzoli & Massimiliano Marcellino, 2012. "The banking and distribution sectors in a small open economy DSGE Model," Working Papers 454, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Massimiliano Marcellino & Yuliya Rychalovska, 2012. "An estimated DSGE model of a Small Open Economy within the Monetary Union: Forecasting and Structural Analysis," RSCAS Working Papers 2012/34, European University Institute.
    2. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.

  65. Ferrara, L. & Marcellino, M. & Mogliani, M., 2012. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," Working papers 383, Banque de France.

    Cited by:

    1. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    2. Knut Lehre Seip & Yunus Yilmaz & Michael Schröder, 2019. "Comparing Sentiment- and Behavioral-Based Leading Indexes for Industrial Production: When Does Each Fail?," Economies, MDPI, vol. 7(4), pages 1-18, October.
    3. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    4. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    5. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    6. Caterina Forti Grazzini & Massimo Guidolin, 2013. "Forecasting yield spreads under crisis-induced multiple breakpoints," Applied Economics Letters, Taylor & Francis Journals, vol. 20(18), pages 1656-1664, December.
    7. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," Working Papers hal-04141344, HAL.
    8. Ana B. Galvão & Michael T. Owyang, 2014. "Financial stress regimes and the macroeconomy," Working Papers 2014-20, Federal Reserve Bank of St. Louis.
    9. Kurmaş Akdoğan, 2017. "Unemployment hysteresis and structural change in Europe," Empirical Economics, Springer, vol. 53(4), pages 1415-1440, December.
    10. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    11. Emilio Zanetti Chini, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," CREATES Research Papers 2018-13, Department of Economics and Business Economics, Aarhus University.
    12. Boris Blagov & Michael Funke & Richhild Moessner, 2015. "Modelling the time-variation in euro area lending spreads," BIS Working Papers 526, Bank for International Settlements.
    13. Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    14. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    15. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    16. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    17. Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.
    18. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    19. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    20. Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
    21. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    22. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions With Boosted Regression Trees," Working Papers 2015-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    23. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    24. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    25. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    26. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    27. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    28. Mahmut Gunay, 2016. "Forecasting Turkish GDP Growth with Financial Variables and Confidence Indicators," CBT Research Notes in Economics 1614, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    29. Kurmaş Akdoğan, 2015. "Asymmetric Behaviour of Inflation around the Target in Inflation-Targeting Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 62(5), pages 486-504, November.
    30. Benjamin Garcia & Arsenios Skaperdas, 2017. "Inferring the Shadow Rate from Real Activity," Finance and Economics Discussion Series 2017-106, Board of Governors of the Federal Reserve System (U.S.).
    31. Jackson, Karen & Magkonis, Georgios, 2024. "Exchange rate predictability: Fact or fiction?," Journal of International Money and Finance, Elsevier, vol. 142(C).

  66. Schumacher, Christian & Marcellino, Massimiliano & Foroni, Claudia, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.

    Cited by:

    1. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    2. Winkelried, Diego, 2012. "Predicting quarterly aggregates with monthly indicators," Working Papers 2012-023, Banco Central de Reserva del Perú.
    3. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    4. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    5. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    6. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    8. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    9. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    10. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    11. Götz, T.B. & Hecq, A.W., 2013. "Nowcasting causality in mixed frequency vector autoregressive models," Research Memorandum 050, Maastricht University, Graduate School of Business and Economics (GSBE).
    12. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    13. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    14. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, April.
    15. Marie Bessec & Othman Bouabdallah, 2015. "Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 360-384, June.
    16. Bonino-Gayoso, Nicolás & García-Hiernaux, Alfredo, 2019. "TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables," MPRA Paper 93366, University Library of Munich, Germany.
    17. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    18. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    19. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    20. Semih Emre Çekin & Victor J. Valcarcel, 2020. "Inflation volatility and inflation in the wake of the great recession," Empirical Economics, Springer, vol. 59(4), pages 1997-2015, October.
    21. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Forecasting GDP with energy series: ADL-MIDAS vs. Linear Time Series Models," Working Papers 035, Centre for Econometric and Allied Research, University of Ibadan.
    22. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2017. "Density Forecasts With Midas Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 783-801, June.
    23. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    24. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    25. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    26. Francisco Blasques & Siem Jan Koopman & Max Mallee, 2014. "Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-105/III, Tinbergen Institute.
    27. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    28. Maxime Leboeuf & Louis Morel, 2014. "Forecasting Short-Term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions," Discussion Papers 14-3, Bank of Canada.
    29. Elena Andreou & Andros Kourtellos, 2015. "The State and the Future of Cyprus Macroeconomic Forecasting," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 9(1), pages 73-90, June.
    30. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
    31. Conefrey, Thomas & Walsh, Graeme, 2018. "A Monthly Indicator of Economic Activity for Ireland," Economic Letters 14/EL/18, Central Bank of Ireland.
    32. Ramazan Yanik & Asfia Binte Osman & Ozcan Ozturk, 2020. "Impact of manufacturing PMI on stock market index: A study on Turkey," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 6(3), pages 104-108.
    33. Heiner Mikosch & Ying Zhang, 2014. "Forecasting Chinese GDP Growth with Mixed Frequency Data," KOF Working papers 14-359, KOF Swiss Economic Institute, ETH Zurich.
    34. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    35. Dirk Drechsel & Stefan Neuwirth, 2016. "Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting," KOF Working papers 16-407, KOF Swiss Economic Institute, ETH Zurich.
    36. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    37. Trujillo-Barrera, Andres & Pennings, Joost M.E., 2013. "Energy and Food Commodity Prices Linkage: An Examination with Mixed-Frequency Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150465, Agricultural and Applied Economics Association.

  67. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.

    Cited by:

    1. Brandon J. Bates & Mikkel Plagborg-Møller & James H. Stock & Mark W. Watson, "undated". "Consistent factor estimation in dynamic factor models with structural instability," Working Paper 84631, Harvard University OpenScholar.
    2. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
    3. Han, Xu & Inoue, Atsushi, 2015. "Tests For Parameter Instability In Dynamic Factor Models," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1117-1152, October.
    4. Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2018. "Forecasting US GNP growth: The role of uncertainty," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 541-559, August.
    5. Eickmeier, Sandra & Ng, Tim, 2011. "How Do Credit Supply Shocks Propagate Internationally? A GVAR approach," CEPR Discussion Papers 8720, C.E.P.R. Discussion Papers.
    6. Prieto, Esteban & Eickmeier, Sandra & Marcellino, Massimiliano, 2013. "Time variation in macro-financial linkages," Discussion Papers 13/2013, Deutsche Bundesbank.
    7. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    8. Han, Xu, 2015. "Tests for overidentifying restrictions in Factor-Augmented VAR models," Journal of Econometrics, Elsevier, vol. 184(2), pages 394-419.
    9. Breitung, Jörg & Eickmeier, Sandra, 2009. "Testing for structural breaks in dynamic factor models," Discussion Paper Series 1: Economic Studies 2009,05, Deutsche Bundesbank.
    10. Gary Koop & Dimitris Korobilis, 2013. "A new index of financial conditions," Working Papers 1307, University of Strathclyde Business School, Department of Economics.
    11. Pappa, Evi & Molteni, Francesco, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," CEPR Discussion Papers 12541, C.E.P.R. Discussion Papers.
    12. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.
    13. Hosszú, Zsuzsanna, 2018. "The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach," Economic Systems, Elsevier, vol. 42(1), pages 32-44.
    14. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    15. Niall O’Sullivan & Sheng Zhu & Jason Foran, 2019. "Sentiment versus liquidity pricing effects in the cross-section of UK stock returns," Journal of Asset Management, Palgrave Macmillan, vol. 20(4), pages 317-329, July.

  68. Stelios Bekiros & Massimiliano Marcellino, 2011. "The Multiscale Causal Dynamics of Foreign Exchange Markets," Economics Working Papers ECO2011/23, European University Institute.

    Cited by:

    1. Sehgal, Sanjay & Pandey, Piyush & Diesting, Florent, 2017. "Examining dynamic currency linkages amongst South Asian economies: An empirical study," Research in International Business and Finance, Elsevier, vol. 42(C), pages 173-190.
    2. Kitamura, Yoshihiro, 2017. "Simple measures of market efficiency: A study in foreign exchange markets," Japan and the World Economy, Elsevier, vol. 41(C), pages 1-16.
    3. Avdoulas, Christos & Bekiros, Stelios & Boubaker, Sabri, 2016. "Detecting nonlinear dependencies in eurozone peripheral equity markets: A multistep filtering approach," Economic Modelling, Elsevier, vol. 58(C), pages 580-587.
    4. Mehmet Balcilar & Rangan Gupta & Duc Khuong Nguyen & Mark E. Wohar, 2018. "Causal effects of the United States and Japan on Pacific-Rim stock markets: nonparametric quantile causality approach," Applied Economics, Taylor & Francis Journals, vol. 50(53), pages 5712-5727, November.
    5. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
    6. Gazi Salah Uddin & Ahmed Taneem Muzaffar & Mohamed Arouri & Bo Sjö, 2017. "Understanding the Relationship between Inflation and Growth: A Wavelet Transformation Approach in the Case of Bangladesh," The World Economy, Wiley Blackwell, vol. 40(9), pages 1918-1933, September.
    7. McNevin, Bruce D. & Nix, Joan, 2018. "The beta heuristic from a time/frequency perspective: A wavelet analysis of the market risk of sectors," Economic Modelling, Elsevier, vol. 68(C), pages 570-585.
    8. Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah & Sjö, Bo, 2016. "On the time scale behavior of equity-commodity links: Implications for portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 30-46.
    9. Matthieu Garcin, 2019. "Hurst Exponents And Delampertized Fractional Brownian Motions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-26, August.
    10. Gong, Yuting & Ma, Chao & Chen, Qiang, 2022. "Exchange rate dependence and economic fundamentals: A Copula-MIDAS approach," Journal of International Money and Finance, Elsevier, vol. 123(C).
    11. Stelios Bekiros & Jose Arreola Hernandez & Gazi Salah Uddin & Ahmed Taneem Muzaffar, 2020. "On the predictability of crude oil market: A hybrid multiscale wavelet approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 599-614, July.
    12. Vogl, Markus, 2022. "Controversy in financial chaos research and nonlinear dynamics: A short literature review," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    13. Bekiros, Stelios & Boubaker, Sabri & Nguyen, Duc Khuong & Uddin, Gazi Salah, 2017. "Black swan events and safe havens: The role of gold in globally integrated emerging markets," Journal of International Money and Finance, Elsevier, vol. 73(PB), pages 317-334.
    14. Tiwari, Aviral Kumar & Trabelsi, Nader & Abakah, Emmanuel Joel Aikins & Nasreen, Samia & Lee, Chien-Chiang, 2023. "An empirical analysis of the dynamic relationship between clean and dirty energy markets," Energy Economics, Elsevier, vol. 124(C).
    15. Gazi Salah Uddin & Muhammad Yahya & Ali Ahmed & Donghyun Park & Shu Tian, 2024. "In search of light in the darkness: What can we learn from ethical, sustainable and green investments?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1451-1495, April.
    16. Uddin, Gazi Salah & Bekiros, Stelios & Ahmed, Ali, 2018. "The nexus between geopolitical uncertainty and crude oil markets: An entropy-based wavelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 30-39.
    17. Liow, Kim Hiang & Huang, Yuting & Song, Jeonseop, 2019. "Relationship between the United States housing and stock markets: Some evidence from wavelet analysis," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    18. Xiaojie Xu, 2018. "Causal structure among US corn futures and regional cash prices in the time and frequency domain," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(13), pages 2455-2480, October.
    19. Faria, Gonçalo & Verona, Fabio, 2020. "Frequency-domain information for active portfolio management," Bank of Finland Research Discussion Papers 2/2020, Bank of Finland.
    20. Rémi Odry & Roman Mestre, 2021. "Monetary Policy and Business Cycle Synchronization in Europe," EconomiX Working Papers 2021-19, University of Paris Nanterre, EconomiX.
    21. Saba Qureshi & Muhammad Aftab, 2023. "Exchange Rate Interdependence in ASEAN Markets: A Wavelet Analysis," Global Business Review, International Management Institute, vol. 24(6), pages 1180-1204, December.
    22. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements," International Review of Economics & Finance, Elsevier, vol. 30(C), pages 101-119.
    23. Nikola Gradojevic, 2021. "Brexit and foreign exchange market expectations: Could it have been predicted?," Annals of Operations Research, Springer, vol. 297(1), pages 167-189, February.
    24. Matthieu Garcin, 2018. "Hurst exponents and delampertized fractional Brownian motions," Working Papers hal-01919754, HAL.
    25. Wu, Tao & Sun, Xiaotong & Xu, Xin & Jia, Nanfei & Xuan, Siyuan, 2024. "New evidence of interdependence in forex markets: A connection of connection analysis," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    26. Caraiani, Petre & Haven, Emmanuel, 2015. "Evidence of multifractality from CEE exchange rates against Euro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 395-407.
    27. Gonçalo Faria & Fabio Verona, 2021. "Time-frequency forecast of the equity premium," Quantitative Finance, Taylor & Francis Journals, vol. 21(12), pages 2119-2135, December.
    28. Rahman, Md Lutfur & Troster, Victor & Uddin, Gazi Salah & Yahya, Muhammad, 2022. "Systemic risk contribution of banks and non-bank financial institutions across frequencies: The Australian experience," International Review of Financial Analysis, Elsevier, vol. 79(C).
    29. Syed Jawad Hussain Shahzad & Jose Arreola‐Hernandez & Md Lutfur Rahman & Gazi Salah Uddin & Muhammad Yahya, 2021. "Asymmetric interdependence between currency markets' volatilities across frequencies and time scales," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2436-2457, April.
    30. Harshit Mishra & Parama Barai, 2024. "Entropy Augmented Asset Pricing Model: Study on Indian Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(1), pages 81-99, March.
    31. Nicoló Andrea Caserini & Paolo Pagnottoni, 2022. "Effective transfer entropy to measure information flows in credit markets," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 729-757, October.
    32. Aloui, Chaker & Hkiri, Besma & Lau, Marco Chi Keung & Yarovaya, Larisa, 2018. "Information transmission across stock indices and stock index futures: International evidence using wavelet framework," Research in International Business and Finance, Elsevier, vol. 44(C), pages 411-421.
    33. Sevda Kuşkaya & Nurhan Toğuç & Faik Bilgili, 2022. "Wavelet coherence analysis and exchange rate movements," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4675-4692, December.
    34. Bilgili, Faik & Kocak, Emrah & Kuskaya, Sevda & Bulut, Umit, 2022. "Co-movements and causalities between ethanol production and corn prices in the USA: New evidence from wavelet transform analysis," Energy, Elsevier, vol. 259(C).
    35. Peterson Owusu Junior & Anokye M. Adam & George Tweneboah, 2017. "Co-movement of real exchange rates in the West African Monetary Zone," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1351807-135, January.
    36. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2020. "Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
    37. Sohel Azad, A.S.M. & Batten, Jonathan A. & Fang, Victor & Wickramanayake, Jayasinghe, 2015. "International swap market contagion and volatility," Economic Modelling, Elsevier, vol. 47(C), pages 355-371.
    38. Al Rababa’a, Abdel Razzaq & Alomari, Mohammad & McMillan, David, 2021. "Multiscale stock-bond correlation: Implications for risk management," Research in International Business and Finance, Elsevier, vol. 58(C).
    39. Jena, Sangram Keshari & Tiwari, Aviral Kumar & Roubaud, David, 2018. "Comovements of gold futures markets and the spot market: A wavelet analysis," Finance Research Letters, Elsevier, vol. 24(C), pages 19-24.
    40. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    41. Palazzi, Rafael Baptista & Júnior, Gerson de Souza Raimundo & Klotzle, Marcelo Cabus, 2021. "The dynamic relationship between bitcoin and the foreign exchange market: A nonlinear approach to test causality between bitcoin and currencies," Finance Research Letters, Elsevier, vol. 42(C).
    42. Habimana, Olivier, 2017. "The multiscale relationship between exchange rates and fundamentals differentials: Empirical evidence from Scandinavia," MPRA Paper 75956, University Library of Munich, Germany.

  69. Szabolcs Deak & Lionel Fontagné & Massimiliano Marcellino & Marco Maffezzoli, 2011. "LSM: A DSGE Model for Luxembourg," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00639888, HAL.

    Cited by:

    1. Donal Smith, 2015. "Collateral Constraints and the Interest Rate," Discussion Papers 15/22, Department of Economics, University of York.
    2. Szabolcs Deák & Lionel Fontagné & Marco Maffezzoli & Massimiliano Marcellino, 2012. "The banking and distribution sectors in a small open economy DSGE Model," Working Papers 454, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Tim Schwarzmüller & Nikolai Stähler, 2013. "Reforming the labor market and improving competitiveness: an analysis for Spain using FiMod," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 4(4), pages 437-471, November.
    4. Alban Moura & Kyriacos Lambrias, 2018. "LU-EAGLE: A DSGE model for Luxembourg within the euro area and global economy," BCL working papers 122, Central Bank of Luxembourg.
    5. Massimiliano Marcellino & Yuliya Rychalovska, 2012. "An estimated DSGE model of a Small Open Economy within the Monetary Union: Forecasting and Structural Analysis," RSCAS Working Papers 2012/34, European University Institute.
    6. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.
    7. Luca Marchiori & Olivier Pierrard, 2012. "LOLA 2.0: Luxembourg OverLapping generation model for policy Analysis," BCL working papers 76, Central Bank of Luxembourg.
    8. Acocella, Nicola & Beqiraj, Elton & Di Bartolomeo, Giovanni & Di Pietro, Marco & Felici, Francesco & Alleva, Giorgio & Di Dio, Fabio & Liseo, Brunero, 2020. "A stochastic estimated version of the Italian dynamic General Equilibrium Model," Economic Modelling, Elsevier, vol. 92(C), pages 339-357.
    9. Ibrahima Sangaré, 2019. "Housing sector and optimal macroprudential policy in an estimated DSGE model for Luxembourg," BCL working papers 129, Central Bank of Luxembourg.
    10. Luca Marchiori & Olivier Pierrard, 2015. "LOLA 3.0: Luxembourg OverLapping generation model for policy Analysis," BCL working papers 100, Central Bank of Luxembourg.
    11. Donal Smith, 2020. "Collateral Constraints and the Interest Rate," Scottish Journal of Political Economy, Scottish Economic Society, vol. 67(2), pages 137-165, May.
    12. Francesco Sergi, 2020. "The Standard Narrative about DSGE Models in Central Banks’ Technical Reports," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 27(2), pages 163-193, March.

  70. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.

    Cited by:

    1. Gent Bajraj & Jorge Lorca & Juan M. Wlasiuk, 2022. "On Foreign Drivers of EMEs Fluctuations," Working Papers Central Bank of Chile 951, Central Bank of Chile.
    2. Sungurtekin Hallam, Bahar, 2022. "Emerging market responses to external shocks: A cross-country analysis," Economic Modelling, Elsevier, vol. 115(C).
    3. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    4. Jorge Lorca, 2021. "Capital Flows and Emerging Markets Fluctuations," Working Papers Central Bank of Chile 898, Central Bank of Chile.
    5. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    6. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    7. Enders, Zeno & Peter, Alexandra, 2015. "Global Banking, Trade, and the International Transmission of the Great Recession," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113022, Verein für Socialpolitik / German Economic Association.
    8. M. Ayhan Kose & Csilla Lakatos & Franziska Ohnsorge & Marc Stocker, 2017. "The Global Role of the U.S. Economy: Linkages, Policies and Spillovers," Koç University-TUSIAD Economic Research Forum Working Papers 1706, Koc University-TUSIAD Economic Research Forum.
    9. Hilde C. Bjørnland & Leif Anders Thorsrud, 2015. "Commodity prices and fiscal policy design: Procyclical despite a rule," Working Papers No 5/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    10. Corina SAMAN, 2016. "The Impact of the US and Euro Area Financial Systemic Stress to the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 170-183, December.
    11. Ellington, Michael, 2018. "Financial market illiquidity shocks and macroeconomic dynamics: Evidence from the UK," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 225-236.
    12. Sithole, Thanda & Simo-Kengne, Beatrice D. & Some, Modeste, 2017. "The role of financial conditions in transmitting external shocks to South Africa," International Economics, Elsevier, vol. 150(C), pages 36-56.
    13. Yamamoto, Shugo, 2014. "Transmission of US financial and trade shocks to Asian economies: Implications for spillover of the 2007–2009 US financial crisis," The North American Journal of Economics and Finance, Elsevier, vol. 27(C), pages 88-103.
    14. Michal Franta & Jan Libich & Petr Stehlík, 2018. "Tracking Monetary-Fiscal Interactions across Time and Space," International Journal of Central Banking, International Journal of Central Banking, vol. 14(3), pages 167-227, June.
    15. Knut Are Aastveit & Hilde C. Bjørnland & Leif Anders Thorsrud, 2011. "The world is not enough! Small open economies and regional dependence," Working Paper 2011/16, Norges Bank.
    16. Antonakakis, Nikolaos & Badinger, Harald, 2012. "Output Volatility, Economic Growth, and Cross-Country Spillovers: New Evidence for the G7 Countries," Department of Economics Working Paper Series 141, WU Vienna University of Economics and Business.
    17. Karlsson, Sune & Österholm, Pär, 2018. "Is the US Phillips Curve Stable? Evidence from Bayesian VARs," Working Papers 2018:5, Örebro University, School of Business.
    18. Liu, Wei & Garrett, Ian, 2023. "Regime-dependent effects of macroeconomic uncertainty on realized volatility in the U.S. stock market," Economic Modelling, Elsevier, vol. 128(C).
    19. Eickmeier, Sandra & Ng, Tim, 2011. "How Do Credit Supply Shocks Propagate Internationally? A GVAR approach," CEPR Discussion Papers 8720, C.E.P.R. Discussion Papers.
    20. Karlsson, Sune & Österholm, Pär, 2019. "The Relation between the Corporate Bond-Yield Spread and the Real Economy: Stable or TimeVarying?," Working Papers 2019:7, Örebro University, School of Business.
    21. Hilberg, Björn & Grill, Michael & Metiu, Norbert, 2016. "Credit constraints and the international propagation of US financial shocks," Working Paper Series 1954, European Central Bank.
    22. Carrillo Julio A. & García Ana Laura, 2021. "The COVID-19 Economic Crisis in Mexico through the Lens of a Financial Conditions Index," Working Papers 2021-23, Banco de México.
    23. Prieto, Esteban & Eickmeier, Sandra & Marcellino, Massimiliano, 2013. "Time variation in macro-financial linkages," Discussion Papers 13/2013, Deutsche Bundesbank.
    24. Valerio Paolo Vacca & Fabian Bichlmeier & Paolo Biraschi & Natalie Boschi & Antonio J. Bravo Alvarez & Luciano Di Primio & André Ebner & Silvia Hoeretzeder & Elisa Llorente Ballesteros & Claudia Mian, 2021. "Measuring the impact of a bank failure on the real economy. An EU-wide analytical framework," Questioni di Economia e Finanza (Occasional Papers) 626, Bank of Italy, Economic Research and International Relations Area.
    25. Knut Are Aastveit & Hilde C. Bjørnland & Leif Anders Thorsrud, 2012. "What drives oil prices? Emerging versus developed economies," Working Paper 2012/11, Norges Bank.
    26. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    27. Yves S. Schüler, 2014. "Asymmetric Effects of Uncertainty over the Business Cycle: A Quantile Structural Vector Autoregressive Approach," Working Paper Series of the Department of Economics, University of Konstanz 2014-02, Department of Economics, University of Konstanz.
    28. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    29. Gary Koop & Dimitris Korobilis, 2013. "A new index of financial conditions," Working Papers 1307, University of Strathclyde Business School, Department of Economics.
    30. Michal Franta & Roman Horvath & Marek Rusnak, 2011. "Evaluating Changes in the Monetary Transmission Mechanism in the Czech Republic," Working Papers 2011/13, Czech National Bank.
    31. Galariotis, Emilios & Makrichoriti, Panagiota & Spyrou, Spyros, 2018. "The impact of conventional and unconventional monetary policy on expectations and sentiment," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 1-20.
    32. Guidolin, Massimo & Hansen, Erwin & Pedio, Manuela, 2019. "Cross-asset contagion in the financial crisis: A Bayesian time-varying parameter approach," Journal of Financial Markets, Elsevier, vol. 45(C), pages 83-114.
    33. Eddie Gerba & Danilo Leiva-Leon, 2020. "Macro-financial interactions in a changing world," Working Papers 2018, Banco de España.
    34. Ying Lun Cheung, 2024. "Identification of Time-Varying Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 76-94, January.
    35. Vespignani, Joaquin L. & Ratti, Ronald A., 2016. "Not all international monetary shocks are alike for the Japanese economy," Economic Modelling, Elsevier, vol. 52(PB), pages 822-837.
    36. Christiane Baumeister & James D. Hamilton, 2020. "Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions," NBER Working Papers 26606, National Bureau of Economic Research, Inc.
    37. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.
    38. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2016. "The Effectiveness of Monetary Policy in South Africa under Inflation Targeting: Evidence from a Time-Varying Factor-Augmented Vector Autoregressive Model," Working Papers 201653, University of Pretoria, Department of Economics.
    39. Gregor Boehl, 2020. "Monetary Policy and Speculative Asset Markets," CRC TR 224 Discussion Paper Series crctr224_2020_224, University of Bonn and University of Mannheim, Germany.
    40. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    41. Hosszú, Zsuzsanna, 2018. "The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach," Economic Systems, Elsevier, vol. 42(1), pages 32-44.
    42. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2018. "The global component of inflation volatility," Temi di discussione (Economic working papers) 1170, Bank of Italy, Economic Research and International Relations Area.
    43. Fink, Fabian & Schüler, Yves S., 2015. "The transmission of US systemic financial stress: Evidence for emerging market economies," Journal of International Money and Finance, Elsevier, vol. 55(C), pages 6-26.
    44. Fonseca, Marcelo Gonçalves da Silva & Pereira, Pedro L. Valls, 2014. "Credit shocks and monetary policy in Brazil: a structural FAVAR approach," Textos para discussão 358, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    45. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    46. Marcel Förster & Markus Jorra & Peter Tillmann, 2012. "The Dynamics of International Capital Flows: Results from a Dynamic Hierarchical Factor Model," MAGKS Papers on Economics 201221, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    47. Tonzer, Lena, 2015. "Cross-border interbank networks, banking risk and contagion," Journal of Financial Stability, Elsevier, vol. 18(C), pages 19-32.
    48. Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2015. "Classical time varying factor-augmented vector auto-regressive models—estimation, forecasting and structural analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 493-533, June.
    49. Guesmi, Khaled & Makrychoriti, Panagiota & Spyrou, Spyros, 2023. "The relationship between climate risk, climate policy uncertainty, and CO2 emissions: Empirical evidence from the US," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 610-628.
    50. Anastasios Evgenidis & Costas Siriopoulos, 2015. "What are the International Channels Through Which a US Policy Shock is Transmitted to The World Economies? Evidence from a Time Varying FAVAR," Working Papers 190, Bank of Greece.
    51. Krokida, Styliani-Iris & Makrychoriti, Panagiota & Spyrou, Spyros, 2020. "Monetary policy and herd behavior: International evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 386-417.
    52. Cesa Bianchi, Ambrogio & Sokol, Andrej, 2017. "Financial shocks, credit spreads and the international credit channel," Bank of England working papers 693, Bank of England.
    53. Antonakakis, N. & Badinger, H., 2016. "Economic growth, volatility, and cross-country spillovers: New evidence for the G7 countries," Economic Modelling, Elsevier, vol. 52(PB), pages 352-365.
    54. Koester, Gerrit B. & Priesmeier, Christoph, 2015. "The Timing and Responsiveness of Fiscal Policy over the Business Cycle in Germany," MPRA Paper 68412, University Library of Munich, Germany.
    55. Ruch,Franz Ulrich, 2020. "Prospects, Risks, and Vulnerabilities in Emerging and Developing Economies : Lessons from the Past Decade," Policy Research Working Paper Series 9181, The World Bank.
    56. Andrej Sokol & Ambrogio Cesa-Bianchi, 2017. "The International Credit Channel of U.S. Monetary Policy and Financial Shocks," 2017 Meeting Papers 724, Society for Economic Dynamics.
    57. Kumar, Ankit & Dash, Pradyumna, 2020. "Changing transmission of monetary policy on disaggregate inflation in India," Economic Modelling, Elsevier, vol. 92(C), pages 109-125.
    58. Jean-François Rouillard, 2015. "International Risk Sharing and Financial Shocks," Cahiers de recherche 15-13, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    59. Matthew Greenwood‐Nimmo & Viet Hoang Nguyen & Eliza Wu, 2021. "On the International Spillover Effects of Country‐Specific Financial Sector Bailouts and Sovereign Risk Shocks," The Economic Record, The Economic Society of Australia, vol. 97(317), pages 285-309, June.
    60. Baumeister, Christiane & Hamilton, James D., 2021. "Reprint: Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 114(C).
    61. Paolo Gorgi & Siem Jan Koopman & Julia Schaumburg, 2021. "Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors," Tinbergen Institute Discussion Papers 21-056/III, Tinbergen Institute.
    62. Beutel, Johannes & Emter, Lorenz & Metiu, Norbert & Prieto, Esteban & Schüler, Yves, 2022. "The global financial cycle and macroeconomic tail risks," Discussion Papers 43/2022, Deutsche Bundesbank.
    63. Takao Asano & Xiaojing Cai & Ryuta Sakemoto, 2023. "Time-varying ambiguity shocks and business cycles," KIER Working Papers 1094, Kyoto University, Institute of Economic Research.
    64. Michael Ellington & Chris Florackis & Costas Milas, 2016. "Liquidity Shocks and Real GDP Growth: Evidence from a Bayesian Time-varying Parameter VAR," Working Paper series 16-28, Rimini Centre for Economic Analysis.
    65. Aastveit, Knut Are, 2014. "Oil price shocks in a data-rich environment," Energy Economics, Elsevier, vol. 45(C), pages 268-279.

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    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    3. Poon, Aubrey & Zhu, Dan, 2022. "Do Recessions Occur Concurrently Across Countries? A Multinomial Logistic Approach," Working Papers 2022:11, Örebro University, School of Business.
    4. Michael Funke & Aaron Mehrotra & Hao Yu, 2015. "Tracking Chinese CPI inflation in real time," Empirical Economics, Springer, vol. 48(4), pages 1619-1641, June.
    5. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    6. Peng, Lijuan & Liang, Chao & Yang, Baoying & Wang, Lu, 2024. "Crude oil volatility forecasting: Insights from a novel time-varying parameter GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 94(C).
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    8. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    9. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    10. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    11. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    12. Xiaochun Liu, 2017. "An integrated macro‐financial risk‐based approach to the stressed capital requirement," Review of Financial Economics, John Wiley & Sons, vol. 34(1), pages 86-98, September.
    13. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    14. Stankevich, Ivan, 2023. "Application of Markov-Switching MIDAS models to nowcasting of GDP and its components," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 122-143.
    15. Xinyu Wang & Cathy Ning, 2022. "A new Markov regime‐switching count time series approach for forecasting initial public offering volumes and detecting issue cycles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 118-133, January.
    16. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    17. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    18. Marie Bessec & Othman Bouabdallah, 2015. "Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 360-384, June.
    19. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    20. Eric Ghysels & Pierre Guérin & Massimiliano Marcellino, 2013. "Regime Switches in the Risk-Return Trade-Off," Staff Working Papers 13-51, Bank of Canada.
    21. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    22. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    23. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    24. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    25. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    26. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    27. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    28. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    29. de Bruijn, L.P. & Franses, Ph.H.B.F., 2015. "Stochastic levels and duration dependence in US unemployment," Econometric Institute Research Papers EI2015-20, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    30. Salvador, Enrique & Floros, Christos & Arago, Vicent, 2014. "Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 60-77.
    31. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    32. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    33. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    34. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    35. Boriss Siliverstovs, 2020. "Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts," Empirical Economics, Springer, vol. 58(1), pages 7-27, January.
    36. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-switching three-pass regression filter," Working Papers 1748, Banco de España.
    37. Marcellino, Massimiliano & Foroni, Claudia & Casarin, Roberto & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    38. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    39. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    40. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    41. Charfeddine, Lanouar & Klein, Tony & Walther, Thomas, 2018. "Oil Price Changes and U.S. Real GDP Growth: Is this Time Different?," QBS Working Paper Series 2018/03, Queen's University Belfast, Queen's Business School.
    42. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
    43. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
    44. Denisa BANULESCU-RADU & Laurent FERRARA & Clément MARSILLI, 2019. "Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences," LEO Working Papers / DR LEO 2710, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    45. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    46. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    47. Ouyang, Ruolan & Chen, Xiang & Fang, Yi & Zhao, Yang, 2022. "Systemic risk of commodity markets: A dynamic factor copula approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    48. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    49. Bjoern Schulte-Tillman & Mawuli Segnon & Bernd Wilfling, 2022. "Financial-market volatility prediction with multiplicative Markov-switching MIDAS components," CQE Working Papers 9922, Center for Quantitative Economics (CQE), University of Muenster.
    50. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
    51. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    52. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    53. You, Yu & Liu, Xiaochun, 2020. "Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach," Journal of Banking & Finance, Elsevier, vol. 116(C).
    54. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    55. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    56. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.

  72. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter, 2011. "On the importance of sectoral and regional shocks for price-setting," CEPR Discussion Papers 8357, C.E.P.R. Discussion Papers.

    Cited by:

    1. Shi Yafeng & Ai Chunrong & Yanlong Shi & Ying Tingting & Xu Qunfang, 2023. "Large covariance estimation using a factor model with common and group‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2217-2248, December.
    2. Poncela, Pilar, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Cristina Conflitti and Matteo Luciani, 2019. "Oil Price Pass-through into Core Inflation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    4. Utku Özmen, Mustafa & Akçelik, Fatih, 2017. "Asymmetric exchange rate and oil price pass-through in motor fuel market: A microeconometric approach," The Journal of Economic Asymmetries, Elsevier, vol. 15(C), pages 64-75.
    5. Carlomagno, Guillermo, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Elena Deryugina & Natalia Karlova & Alexey Ponomarenko & Anna Tsvetkova, 2019. "The role of regional and sectoral factors in Russian inflation developments," Economic Change and Restructuring, Springer, vol. 52(4), pages 453-474, November.
    7. Pino, Gabriel, 2013. "Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain," DES - Working Papers. Statistics and Econometrics. WS ws130807, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Firouz Fallahi, 2019. "Persistence and stationarity of sectoral energy consumption in the US: A confidence interval approach," Energy & Environment, , vol. 30(5), pages 882-897, August.
    9. Firouz Fallahi, 2020. "Persistence and unit root in $$\text {CO}_{2}$$CO2 emissions: evidence from disaggregated global and regional data," Empirical Economics, Springer, vol. 58(5), pages 2155-2179, May.
    10. Carlomagno, Guillermo & Eterovic, Nicolás & Hernández-Román, Luis G., 2024. "Disentangling demand and supply inflation shocks from electronic payments data," Economic Modelling, Elsevier, vol. 141(C).
    11. Chong, Terence Tai Leung & Wu, Zhang, 2018. "Price Rigidity in China: Empirical Results at Home and Abroad," MPRA Paper 92013, University Library of Munich, Germany.
    12. Sala, Hector & Trivín, Pedro, 2013. "Labour Market Dynamics in Spanish Regions: Evaluating Asymmetries in Troublesome Times," IZA Discussion Papers 7746, Institute of Labor Economics (IZA).
    13. Raphael A. Auer & Andrei A. Levchenko & Philip Sauré, 2017. "International Inflation Spillovers Through Input Linkages," NBER Working Papers 23246, National Bureau of Economic Research, Inc.
    14. Alessandro Cantelmo & Giovanni Melina, 2015. "Monetary Policy and the Relative Price of Durable Goods," CESifo Working Paper Series 5328, CESifo.
    15. Helena Marques & Gabriel Pino & J.D.Tena, 2009. "Regional inflation dynamics using space-time models," DEA Working Papers 40, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    16. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2019. "Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure," SERIES 03-2019, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2019.
    17. Kaufmann, Daniel & Lein, Sarah M., 2013. "Sticky prices or rational inattention – What can we learn from sectoral price data?," European Economic Review, Elsevier, vol. 64(C), pages 384-394.
    18. Guillermo Carlomagno & Nicolas Eterovic & L. G. Hernández-Román, 2023. "Disentangling Demand and Supply Inflation Shocks from Chilean Electronic Payment Data," Working Papers Central Bank of Chile 986, Central Bank of Chile.
    19. Choi, In & Lin, Rui & Shin, Yongcheol, 2023. "Canonical correlation-based model selection for the multilevel factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 22-44.
    20. Alex Nikolsko‐Rzhevskyy & Oleksandr Talavera & Nam Vu, 2023. "The flood that caused a drought," Economic Inquiry, Western Economic Association International, vol. 61(4), pages 965-981, October.
    21. Paweł Gajewski, 2017. "Sources of Regional Inflation in Poland," Eastern European Economics, Taylor & Francis Journals, vol. 55(3), pages 261-276, May.
    22. Jörg Breitung & Sandra Eickmeier, 2014. "Analyzing business and financial cycles using multi-level factor models," CAMA Working Papers 2014-43, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    23. Aleksandra Halka & Grzegorz Szafranski, 2018. "What Common Factors are Driving Inflation in CEE Countries?," Prague Economic Papers, Prague University of Economics and Business, vol. 2018(2), pages 131-148.
    24. Marcel Förster & Markus Jorra & Peter Tillmann, 2012. "The Dynamics of International Capital Flows: Results from a Dynamic Hierarchical Factor Model," MAGKS Papers on Economics 201221, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    25. Carlomagno, Guillermo, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    26. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Inference of Grouped Time-Varying Network Vector Autoregression Models," Monash Econometrics and Business Statistics Working Papers 5/23, Monash University, Department of Econometrics and Business Statistics.
    27. Senra, Eva, 2017. "22 Years of inflation assessment and forecasting experience at the bulletin of EU & US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS 24678, Universidad Carlos III de Madrid. Departamento de Estadística.
    28. Antoni Espasa & Eva Senra, 2017. "Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis," Econometrics, MDPI, vol. 5(4), pages 1-28, October.
    29. Kristina Barauskaite & Anh D.M. Nguyen, 2019. "Intersectoral Network-Based Channel of Aggregate TFP Shocks," Bank of Lithuania Working Paper Series 63, Bank of Lithuania.
    30. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Estimation of Grouped Time-Varying Network Vector Autoregression Models," Papers 2303.10117, arXiv.org, revised Mar 2024.

  73. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2011. "Bayesian VARs: Specification Choices and Forecast Accuracy," CEPR Discussion Papers 8273, C.E.P.R. Discussion Papers.

    Cited by:

    1. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    2. Hashmat Khan & Santosh Upadhayaya, 2017. "Does Business Confidence Matter for Investment?," Carleton Economic Papers 17-13, Carleton University, Department of Economics, revised 20 Mar 2019.
    3. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    4. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2020. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 934-943, September.
    5. Oriol Gonzalez-Casasus & Frank Schorfheide, 2025. "Misspecification-Robust Shrinkage and Selection for VAR Forecasts and IRFs," PIER Working Paper Archive 25-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    6. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
    7. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    8. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    9. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    10. Salzmann, Leonard, 2020. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224588, Verein für Socialpolitik / German Economic Association.
    11. Anna Staszewska-Bystrova & Peter Winker, 2014. "Measuring Forecast Uncertainty of Corporate Bond Spreads by Bonferroni-Type Prediction Bands," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(2), pages 89-104, June.
    12. Joshua C. C. Chan, 2024. "BVARs and stochastic volatility," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 3, pages 43-67, Edward Elgar Publishing.
    13. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    14. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    15. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    16. Heather Anderson & Giovanni Caggiano & Farshid Vahid & Benjamin Wong, 2020. "Sectoral employment dynamics in Australia," CAMA Working Papers 2020-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    18. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    19. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    20. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    21. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    22. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2014. "Large Bayesian VARMAs," SIRE Discussion Papers 2015-06, Scottish Institute for Research in Economics (SIRE).
    23. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    24. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    25. Koop, Gary & Korobilis, Dimitris, 2012. "Large time-varying parameter VARs," MPRA Paper 38591, University Library of Munich, Germany.
    26. Tomasz Wozniak, 2016. "Rare Events and Risk Perception: Evidence from Fukushima Accident," Department of Economics - Working Papers Series 2021, The University of Melbourne.
    27. Gary Koop, 2012. "Using VARs and TVP-VARs with Many Macroeconomic Variables," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(3), pages 143-167, September.
    28. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.
    29. Niko Hauzenberger & Florian Huber & Luca Onorante, 2021. "Combining shrinkage and sparsity in conjugate vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 304-327, April.
    30. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, vol. 9(2), pages 1-35, May.
    31. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    32. Berg Tim Oliver, 2017. "Forecast accuracy of a BVAR under alternative specifications of the zero lower bound," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-29, April.
    33. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    34. Che, Ming & Zhu, Zixiang & Li, Yujia, 2023. "Geopolitical risk and economic policy uncertainty: Different roles in China's financial cycle," International Review of Financial Analysis, Elsevier, vol. 90(C).
    35. Berg, Tim Oliver, 2015. "Multivariate Forecasting with BVARs and DSGE Models," MPRA Paper 62405, University Library of Munich, Germany.
    36. Giannoni, Marc & Boivin, Jean & Stevanovic, Dalibor, 2013. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," CEPR Discussion Papers 9470, C.E.P.R. Discussion Papers.
    37. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    38. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
    39. Gu, Xin & Zhu, Zixiang & Yu, Minli, 2021. "The macro effects of GPR and EPU indexes over the global oil market—Are the two types of uncertainty shock alike?," Energy Economics, Elsevier, vol. 100(C).
    40. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," CAMA Working Papers 2014-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    41. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.
    42. Brent Meyer & Saeed Zaman, 2019. "The usefulness of the median CPI in Bayesian VARs used for macroeconomic forecasting and policy," Empirical Economics, Springer, vol. 57(2), pages 603-630, August.
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    44. Giacomo Rella, 2021. "The Fed, housing and household debt over time," Department of Economics University of Siena 850, Department of Economics, University of Siena.
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  74. Marcellino, Massimiliano & Musso, Alberto, 2010. "The Forecasting Performance of Real Time Estimates of the Euro Area Output Gap," CEPR Discussion Papers 7763, C.E.P.R. Discussion Papers.

    Cited by:

    1. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2012. "Business Cycle Synchronization in the Euro Area and the Impact of the Financial Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 33-60.
    2. Jed Armstrong, 2015. "The Reserve Bank of New Zealand’s output gap indicator suite and its real-time properties," Reserve Bank of New Zealand Analytical Notes series AN2015/08, Reserve Bank of New Zealand.
    3. Massimiliano Marcellino & Alberto Musso, 2010. "the Reliability of Real Time Estimates of the EURO Area Output Gap," Economics Working Papers ECO2010/06, European University Institute.
    4. Raffinot, Thomas, 2017. "Interest-Rates-Free Monetary Policy Rule," Working Papers 06898, George Mason University, Mercatus Center.
    5. Kukk Merike & Staehr Karsten, 2015. "Enhanced Fiscal Governance in the European Union: The Fiscal Compact," TalTech Journal of European Studies, Sciendo, vol. 5(1), pages 73-92, February.
    6. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    7. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    8. Marcin Wolski, 2016. "Welfare-theoretic Optimal Policies in a New-Keynesian Economy with Heterogeneous Regions: Any Role for Financial Integration?," Journal of Common Market Studies, Wiley Blackwell, vol. 54(3), pages 742-761, May.
    9. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 363-393, March.
    10. João Sousa Andrade & António Portugal Duarte, 2014. "Output-gaps in the PIIGS Economies: An Ingredient of a Greek Tragedy," GEMF Working Papers 2014-06, GEMF, Faculty of Economics, University of Coimbra.
    11. Czudaj, Robert, 2011. "P-star in times of crisis - Forecasting inflation for the euro area," Economic Systems, Elsevier, vol. 35(3), pages 390-407, September.
    12. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    13. Punnoose Jacob & Finn Robinson, 2019. "Suite as! Augmenting the Reserve Bank’s output gap indicator suite," Reserve Bank of New Zealand Analytical Notes series AN2019/08, Reserve Bank of New Zealand.
    14. Kuusi, Tero, 2018. "Does the structural budget balance guide fiscal policy pro-cyclically? Evidence from the Finnish Great Depression of the 1990s," MPRA Paper 84829, University Library of Munich, Germany.
    15. Otmar Issing, 2010. "The development of monetary policy in the 20th century – some reflections," Working Paper Research 186, National Bank of Belgium.
    16. Agnello, Luca & Castro, Vítor & Sousa, Ricardo M., 2023. "A quest between fiscal and market discipline," Economic Modelling, Elsevier, vol. 119(C).
    17. Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.
    18. Christian Gayer & Bertrand Marc, 2018. "A ‘New Modesty’? Level Shifts in Survey Data and the Decreasing Trend of ‘Normal’ Growth," European Economy - Discussion Papers 083, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    19. Nicolas Pinkwart, 2013. "Quantifying The European Central Bank'S Interest Rate Smoothing Behavior," Manchester School, University of Manchester, vol. 81(4), pages 470-492, July.

  75. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.

    Cited by:

    1. Haroon Mumtaz & Alexandra Solovyeva & Elena Vasilieva, 2012. "Asset prices, credit and the Russian economy," Joint Research Papers 1, Centre for Central Banking Studies, Bank of England.
    2. Kapetanios, George & Mumtaz, Haroon & Stevens, Ibrahim & Theodoridis, Konstantinos, 2012. "Assessing the economy-wide effects of quantitative easing," Bank of England working papers 443, Bank of England.
    3. Cristina Fuentes-Albero & Leonardo Melosi, 2011. "Methods for Computing Marginal Data Densities from the Gibbs Output," Departmental Working Papers 201131, Rutgers University, Department of Economics.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    5. Habrov, Vladimir, 2012. "Optimization of portfolio management based on vector autoregression models and multivariate volatility models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 28(4), pages 35-62.
    6. Hajer Ben Romdhane & Nahed Ben Tanfous, 2017. "Conditional FAVAR and scenario analysis for a large data: case of Tunisia," IHEID Working Papers 15-2017, Economics Section, The Graduate Institute of International Studies.
    7. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    8. Simon Gilchrist & Egon Zakrajsek & Cristina Fuentes Albero & Dario Caldara, 2013. "On the Identification of Financial and Uncertainty Shocks," 2013 Meeting Papers 965, Society for Economic Dynamics.

  76. Marcellino, Massimiliano & Galvão, Ana Beatriz, 2010. "Endogenous Monetary Policy Regimes and the Great Moderation," CEPR Discussion Papers 7827, C.E.P.R. Discussion Papers.

    Cited by:

    1. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    2. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
    3. Jolejole-Foreman, Maria Christina & Mallory, Mindy L. & Baylis, Katherine R., 2013. "Impact of Wheat and Rice Export Ban on Indian Market Integration," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150595, Agricultural and Applied Economics Association.
    4. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    5. Jouchi Nakajima, 2011. "Monetary Policy Transmission under Zero Interest Rates: An Extended Time-Varying Parameter Vector Autoregression Approach," IMES Discussion Paper Series 11-E-08, Institute for Monetary and Economic Studies, Bank of Japan.
    6. Ahmad Yamin & Donayre Luiggi, 2016. "Outliers and persistence in threshold autoregressive processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 37-56, February.
    7. Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2015. "Classical time varying factor-augmented vector auto-regressive models—estimation, forecasting and structural analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 493-533, June.
    8. Jouchi Nakajima & Mike West, 2013. "Bayesian Analysis of Latent Threshold Dynamic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 151-164, April.

  77. Marcellino, Massimiliano & Musso, Alberto, 2010. "The Reliability of Real Time Estimates of the Euro Area Output Gap," CEPR Discussion Papers 7716, C.E.P.R. Discussion Papers.

    Cited by:

    1. Buncic, Daniel & Müller, Oliver, 2017. "Measuring the output gap in Switzerland with linear opinion pools," Economic Modelling, Elsevier, vol. 64(C), pages 153-171.
    2. Alessandro Cianci, 2016. "Disoccupazione strutturale in Italia e regole europee di bilancio," a/ Working Papers Series 1601, Italian Association for the Study of Economic Asymmetries, Rome (Italy).
    3. Cronin, David & McQuinn, Kieran, 2020. "Are official forecasts of output growth in the EU still biased? Evidence from stability and convergence programmes and the European Commission’s Spring forecasts," Papers WP681, Economic and Social Research Institute (ESRI).
    4. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2012. "Business Cycle Synchronization in the Euro Area and the Impact of the Financial Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 33-60.
    5. Jed Armstrong, 2015. "The Reserve Bank of New Zealand’s output gap indicator suite and its real-time properties," Reserve Bank of New Zealand Analytical Notes series AN2015/08, Reserve Bank of New Zealand.
    6. L. Marattin & S. Meraglia, 2015. "Potential Output and Fiscal Rules in a Monetary Union under Asymmetric Information," Working Papers wp1018, Dipartimento Scienze Economiche, Universita' di Bologna.
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    16. Maritta Paloviita & Markus Haavio & Pirkka Jalasjoki & Juha Kilponen, 2021. "What Does "Below, but Close to, 2 Percent" Mean? Assessing the ECB's Reaction Function with Real-Time Data," International Journal of Central Banking, International Journal of Central Banking, vol. 17(2), pages 125-169, June.
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    32. Kukk Merike & Staehr Karsten, 2015. "Enhanced Fiscal Governance in the European Union: The Fiscal Compact," TalTech Journal of European Studies, Sciendo, vol. 5(1), pages 73-92, February.
    33. L. Marattin & S. Meraglia, 2016. "Potential Output and Fiscal Rules in a Monetary Union under Asymmetric Information 2nd ed," Working Papers wp1063, Dipartimento Scienze Economiche, Universita' di Bologna.
    34. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    35. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
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    38. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 363-393, March.
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    41. Hieu Thanh Nguyen & Hiep Ngoc Luu & Ngoc Ha Do, 2021. "The dynamic relationship between greenfield investments, cross-border M&As, domestic investment and economic growth in Vietnam," Economic Change and Restructuring, Springer, vol. 54(4), pages 1065-1089, November.
    42. João Sousa Andrade & António Portugal Duarte, 2014. "Output-gaps in the PIIGS Economies: An Ingredient of a Greek Tragedy," GEMF Working Papers 2014-06, GEMF, Faculty of Economics, University of Coimbra.
    43. Bofinger, Peter & Schnabel, Isabel & Feld, Lars P. & Schmidt, Christoph M. & Wieland, Volker, 2017. "Für eine zukunftsorientierte Wirtschaftspolitik. Jahresgutachten 2017/18 [Towards a Forward-Looking Economic Policy. Annual Report 2017/18]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201718.
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    46. Ley, Eduardo & Misch, Florian, 2013. "Real-time macro monitoring and fiscal policy," Policy Research Working Paper Series 6303, The World Bank.
    47. Breuer, Sebastian & Elstner, Steffen, 2017. "Die Wachstumsperspektiven der deutschen Wirtschaft vor dem Hintergrund des demografischen Wandels: Die Mittelfristprojektion des Sachverständigenrates," Working Papers 07/2017, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    48. Amy Y. Guisinger & Michael T. Owyang & Hannah Shell, 2018. "Comparing Measures of Potential Output," Review, Federal Reserve Bank of St. Louis, vol. 100(4), pages 297-316.
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    50. Czudaj, Robert, 2011. "P-star in times of crisis - Forecasting inflation for the euro area," Economic Systems, Elsevier, vol. 35(3), pages 390-407, September.
    51. Julien Champagne & Guillaume Poulin‐Bellisle & Rodrigo Sekkel, 2018. "The Real‐Time Properties of the Bank of Canada's Staff Output Gap Estimates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(6), pages 1167-1188, September.
    52. Ramis Khabibullin, 2019. "What measures of real economic activity slack are helpful for forecasting Russian inflation?," Bank of Russia Working Paper Series wps50, Bank of Russia.
    53. Ms. Burcu Hacibedel & Pierre Mandon & Ms. Priscilla S Muthoora & Nathalie Pouokam, 2019. "Inequality in Good and Bad Times: A Cross-Country Approach," IMF Working Papers 2019/020, International Monetary Fund.
    54. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    55. Kai Carstensen & Felix Kießner & Thies Rossian, 2023. "Estimation of the TFP Gap for the Largest Five EMU Countries," CESifo Working Paper Series 10245, CESifo.
    56. Jens Boysen-Hogrefe, 2015. "Konjunkturbereinigungsverfahren der Länder: Eine Quasi-Echtzeitanalyse am Beispiel Schleswig-Holsteins," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(1), pages 41-57, April.
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    58. Galimberti, Jaqueson K. & Moura, Marcelo L., 2016. "Improving the reliability of real-time output gap estimates using survey forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 358-373.
    59. Josefine Quast & Maik H. Wolters, 2023. "The Federal Reserve's output gap: The unreliability of real‐time reliability tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 1101-1111, November.
    60. Punnoose Jacob & Finn Robinson, 2019. "Suite as! Augmenting the Reserve Bank’s output gap indicator suite," Reserve Bank of New Zealand Analytical Notes series AN2019/08, Reserve Bank of New Zealand.
    61. Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    62. Kuusi, Tero, 2018. "Does the structural budget balance guide fiscal policy pro-cyclically? Evidence from the Finnish Great Depression of the 1990s," MPRA Paper 84829, University Library of Munich, Germany.
    63. Plödt, Martin & Reicher, Claire, 2014. "Estimating simple fiscal policy reaction functions for the euro area countries," Kiel Working Papers 1899, Kiel Institute for the World Economy (IfW Kiel).
    64. Kempkes, Gerhard, 2012. "Cyclical adjustment in fiscal rules: Some evidence on real-time bias for EU-15 countries," Discussion Papers 15/2012, Deutsche Bundesbank.
    65. Otmar Issing, 2010. "The development of monetary policy in the 20th century – some reflections," Working Paper Research 186, National Bank of Belgium.
    66. Agnello, Luca & Castro, Vítor & Sousa, Ricardo M., 2023. "A quest between fiscal and market discipline," Economic Modelling, Elsevier, vol. 119(C).
    67. Finn Robinson & Jamie Culling & Gael Price, 2019. "Evaluating indicators of labour market capacity in New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2019/09, Reserve Bank of New Zealand.
    68. Lise Pichette & Marie-Noëlle Robitaille & Mohanad Salameh & Pierre St-Amant, 2018. "Dismiss the Gap? A Real-Time Assessment of the Usefulness of Canadian Output Gaps in Forecasting Inflation," Staff Working Papers 18-10, Bank of Canada.
    69. Mr. Jiaqian Chen & Lucyna Gornicka, 2020. "Measuring Output Gap: Is It Worth Your Time?," IMF Working Papers 2020/024, International Monetary Fund.
    70. Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.
    71. Christian Gayer & Bertrand Marc, 2018. "A ‘New Modesty’? Level Shifts in Survey Data and the Decreasing Trend of ‘Normal’ Growth," European Economy - Discussion Papers 083, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    72. Jaqueson K. Galimberti & Marcelo L. Moura, 2011. "Improving the reliability of real-time Hodrick-Prescott filtering using survey forecasts," Centre for Growth and Business Cycle Research Discussion Paper Series 159, Economics, The University of Manchester.
    73. International Monetary Fund, 2014. "Russian Federation: Selected Issues," IMF Staff Country Reports 2014/176, International Monetary Fund.
    74. Ricci-Risquete, Alejandro & Ramajo, Julián & de Castro, Francisco, 2016. "Do Spanish fiscal regimes follow the euro-area trends? Evidence from Markov-Switching fiscal rules," Economic Modelling, Elsevier, vol. 59(C), pages 484-494.
    75. Nicolas Pinkwart, 2013. "Quantifying The European Central Bank'S Interest Rate Smoothing Behavior," Manchester School, University of Manchester, vol. 81(4), pages 470-492, July.
    76. Cronin, David & McInerney, Niall, 2023. "Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment," European Journal of Political Economy, Elsevier, vol. 76(C).
    77. Paloviita, Maritta & Haavio, Markus & Jalasjoki, Pirkka & Kilponen, Juha, 2017. "What does "below, but close to, two percent" mean? Assessing the ECB's reaction function with real time data," Bank of Finland Research Discussion Papers 29/2017, Bank of Finland.
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    79. Marcell Göttert & Timo Wollmershäuser, 2022. "Strukturelle Finanzierungssalden auf verlässliche Füße stellen [Putting Structural Budget Balances on Reliable Footing]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 102(2), pages 108-113, February.
    80. Rünstler, Gerhard & Balfoussia, Hiona & Burlon, Lorenzo & Buss, Ginters & Comunale, Mariarosaria & De Backer, Bruno & Dewachter, Hans & Guarda, Paolo & Haavio, Markus & Hindrayanto, Irma & Iskrev, Nik, 2018. "Real and financial cycles in EU countries - Stylised facts and modelling implications," Occasional Paper Series 205, European Central Bank.

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    Cited by:

    1. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
    2. Tara Sinclair & Herman O. Stekler & Warren Carrow, 2012. "Evaluating a Vector of the Fed's Forecasts," Working Papers 2012-3, The George Washington University, Institute for International Economic Policy.
    3. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    4. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    5. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    6. Tara Sinclair & Herman O. Stekler & Warren Carnow, 2012. "A New Approach For Evaluating Economic Forecasts," Working Papers 2012-2, The George Washington University, Institute for International Economic Policy.
    7. Svetlana Makarova, 2014. "Risk and Uncertainty: Macroeconomic Perspective," UCL SSEES Economics and Business working paper series 129, UCL School of Slavonic and East European Studies (SSEES).
    8. Hans Christian Müller-Dröge & Tara M. Sinclair & Herman O. Stekler, 2014. "Evaluating Forecasts Of A Vector Of Variables: A German Forecasting Competition," Working Papers 2014-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    10. Ilhan Kilic & Faruk Balli, 2024. "Measuring economic country-specific uncertainty in Türkiye," Empirical Economics, Springer, vol. 67(4), pages 1649-1689, October.
    11. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    12. Thomai Filippeli, 2011. "Theoretical Priors for BVAR Models & Quasi-Bayesian DSGE Model Estimation," 2011 Meeting Papers 396, Society for Economic Dynamics.
    13. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
    14. Filippeli, Thomai & Harrison, Richard & Theodoridis, Konstantinos, 2018. "DSGE-based Priors for BVARs & Quasi-Bayesian DSGE Estimation," Cardiff Economics Working Papers E2018/5, Cardiff University, Cardiff Business School, Economics Section.
    15. Knüppel, Malte, 2018. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," International Journal of Forecasting, Elsevier, vol. 34(1), pages 105-116.
    16. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    17. Michael Wolf & Dan Wunderli, 2012. "Bootstrap joint prediction regions," ECON - Working Papers 064, Department of Economics - University of Zurich, revised May 2013.
    18. Filippeli, Thomai & Harrison, Richard & Theodoridis, Konstantinos, 2020. "DSGE-based priors for BVARs and quasi-Bayesian DSGE estimation," Econometrics and Statistics, Elsevier, vol. 16(C), pages 1-27.
    19. Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Tara M. Sinclair & H.O. Stekler, 2011. "Differences in Early GDP Component Estimates Between Recession and Expansion," Working Papers 2011-05, The George Washington University, Institute for International Economic Policy.

  79. Marcellino, Massimiliano & Musso, Alberto, 2010. "Real time estimates of the euro area output gap: reliability and forecasting performance," Working Paper Series 1157, European Central Bank.

    Cited by:

    1. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2012. "Business Cycle Synchronization in the Euro Area and the Impact of the Financial Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 33-60.
    2. Massimiliano Marcellino & Alberto Musso, 2010. "the Reliability of Real Time Estimates of the EURO Area Output Gap," Economics Working Papers ECO2010/06, European University Institute.
    3. Raffinot, Thomas, 2017. "Interest-Rates-Free Monetary Policy Rule," Working Papers 06898, George Mason University, Mercatus Center.
    4. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    5. Kukk Merike & Staehr Karsten, 2015. "Enhanced Fiscal Governance in the European Union: The Fiscal Compact," TalTech Journal of European Studies, Sciendo, vol. 5(1), pages 73-92, February.
    6. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    7. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    8. Marcin Wolski, 2016. "Welfare-theoretic Optimal Policies in a New-Keynesian Economy with Heterogeneous Regions: Any Role for Financial Integration?," Journal of Common Market Studies, Wiley Blackwell, vol. 54(3), pages 742-761, May.
    9. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 363-393, March.
    10. João Sousa Andrade & António Portugal Duarte, 2014. "Output-gaps in the PIIGS Economies: An Ingredient of a Greek Tragedy," GEMF Working Papers 2014-06, GEMF, Faculty of Economics, University of Coimbra.
    11. Czudaj, Robert, 2011. "P-star in times of crisis - Forecasting inflation for the euro area," Economic Systems, Elsevier, vol. 35(3), pages 390-407, September.
    12. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    13. Dr. James Mitchell, 2009. "Measuring Output Gap Uncertainty," National Institute of Economic and Social Research (NIESR) Discussion Papers 342, National Institute of Economic and Social Research.
    14. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michele Modugno, 2012. "An Area-Wide Real-Time Database for the Euro Area," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1000-1013, November.
    15. Punnoose Jacob & Finn Robinson, 2019. "Suite as! Augmenting the Reserve Bank’s output gap indicator suite," Reserve Bank of New Zealand Analytical Notes series AN2019/08, Reserve Bank of New Zealand.
    16. Kuusi, Tero, 2018. "Does the structural budget balance guide fiscal policy pro-cyclically? Evidence from the Finnish Great Depression of the 1990s," MPRA Paper 84829, University Library of Munich, Germany.
    17. Otmar Issing, 2010. "The development of monetary policy in the 20th century – some reflections," Working Paper Research 186, National Bank of Belgium.
    18. Agnello, Luca & Castro, Vítor & Sousa, Ricardo M., 2023. "A quest between fiscal and market discipline," Economic Modelling, Elsevier, vol. 119(C).
    19. Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.
    20. Christian Gayer & Bertrand Marc, 2018. "A ‘New Modesty’? Level Shifts in Survey Data and the Decreasing Trend of ‘Normal’ Growth," European Economy - Discussion Papers 083, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    21. Nicolas Pinkwart, 2013. "Quantifying The European Central Bank'S Interest Rate Smoothing Behavior," Manchester School, University of Manchester, vol. 81(4), pages 470-492, July.
    22. Lucian Croitoru, 2014. "Will there be Deflation and Current Account Surpluses?," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-21, October.
    23. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.

  80. Marcellino, Massimiliano & Kapetanios, George, 2010. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," CEPR Discussion Papers 7726, C.E.P.R. Discussion Papers.

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    1. Ludovica Gambaro & Guido Neidhöfer & C. Katharina Spieß, 2019. "The Effect of Early Childhood Education and Care Services on the Social Integration of Refugee Families," Discussion Papers of DIW Berlin 1828, DIW Berlin, German Institute for Economic Research.
    2. Milda Norkute, 2015. "Can the sectoral New Keynesian Phillips curve explain inflation dynamics in the Euro Area?," Empirical Economics, Springer, vol. 49(4), pages 1191-1216, December.
    3. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2012. "Exponent of Cross-sectional Dependence: Estimation and Inference," CESifo Working Paper Series 3722, CESifo.
    4. Gründler, Klaus & Scheuermeyer, Philipp, 2018. "Growth effects of inequality and redistribution: What are the transmission channels?," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 293-313.
    5. Onatski, Alexei, 2015. "Asymptotic analysis of the squared estimation error in misspecified factor models," Journal of Econometrics, Elsevier, vol. 186(2), pages 388-406.
    6. Wenxi Lu, 2018. "FDI, Service imports and Export development," School of Economics and Public Policy Working Papers 2018-05, University of Adelaide, School of Economics and Public Policy.
    7. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    8. Guy Tchuente, 2016. "Estimation of social interaction models using regularization," Studies in Economics 1607, School of Economics, University of Kent.
    9. Yongfu Huang & Muhammad G. Quibria, 2015. "The global partnership for sustainable development," Natural Resources Forum, Blackwell Publishing, vol. 39(3-4), pages 157-174, August.
    10. Mavroeidis, Sophocles & Plagborg-Moller, Mikkel & Stock, James H., 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Scholarly Articles 22795845, Harvard University Department of Economics.
    11. Pesaran, M. Hashem & Tosetti, Elisa, 2007. "Large Panels with Common Factors and Spatial Correlations," IZA Discussion Papers 3032, Institute of Labor Economics (IZA).
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    1. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    2. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    3. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    4. John W. Galbraith & Victoria Zinde-Walsh, 2011. "Partially Dimension-Reduced Regressions with Potentially Infinite-Dimensional Processes," CIRANO Working Papers 2011s-57, CIRANO.
    5. László Békési & Lorant Kaszab & Szabolcs Szentmihályi, 2017. "The EAGLE model for Hungary - a global perspective," MNB Working Papers 2017/7, Magyar Nemzeti Bank (Central Bank of Hungary).
    6. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2010. "Forecasting with Factor-augmented Error Correction Models," CEPR Discussion Papers 7677, C.E.P.R. Discussion Papers.
    7. Carlomagno, Guillermo, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Galbraith, John W. & Zinde-Walsh, Victoria, 2020. "Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 609-632.
    9. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    10. von Borstel, Julia & Eickmeier, Sandra & Krippner, Leo, 2016. "The interest rate pass-through in the euro area during the sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 386-402.
    11. Chris Bloor & Troy Matheson, 2010. "Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand," Empirical Economics, Springer, vol. 39(2), pages 537-558, October.
    12. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    13. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    14. Claudia Godbout & Marco J. Lombardi, 2012. "Short-Term Forecasting of the Japanese Economy Using Factor Models," Staff Working Papers 12-7, Bank of Canada.
    15. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    16. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    17. Manisha Pradhananga, 2016. "Financialization and the rise in co-movement of commodity prices," International Review of Applied Economics, Taylor & Francis Journals, vol. 30(5), pages 547-566, September.
    18. Mahamadou Roufahi Tankari & Anatole Goundan, 2018. "Nontraded food commodity spatial price transmission: evidence from the Niger millet market," Agricultural Economics, International Association of Agricultural Economists, vol. 49(2), pages 147-156, March.
    19. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working papers 2011-02, University of Connecticut, Department of Economics, revised Aug 2012.
    20. Tibor Szendrei & Katalin Varga, 2017. "FISS - A Factor Based Index of Systemic Stress in the Financial System," MNB Working Papers 2017/9, Magyar Nemzeti Bank (Central Bank of Hungary).
    21. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
    22. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    23. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
    24. Smith, Ron P. & Zoega, Gylfi, 2007. "Global Factors, Unemployment Adjustment and the Natural Rate," Economics Discussion Papers 2007-48, Kiel Institute for the World Economy (IfW Kiel).
    25. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    26. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    27. Dibyendu Maiti & Naveen Kumar & Debajit Jha & Soumyadipta Sarkar, 2024. "Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1095-1120, March.
    28. Giovanni Melina & Stefania Villa, 2014. "Fiscal Policy And Lending Relationships," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 696-712, April.
    29. In Choi & Hanbat Jeong, 2020. "Differencing versus nondifferencing in factor‐based forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 728-750, September.
    30. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    31. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
    32. Gao, Zhaoxing & Tsay, Ruey S., 2021. "Modeling high-dimensional unit-root time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1535-1555.
    33. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
    34. Lombardi, Marco J. & Osbat, Chiara & Schnatz, Bernd, 2010. "Global commodity cycles and linkages a FAVAR approach," Working Paper Series 1170, European Central Bank.
    35. Moosa, Imad A. & Vaz, John J., 2016. "Cointegration, error correction and exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 21-34.
    36. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    37. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    38. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    39. Bardhyl Dauti, 2024. "Macroeconomic, institutional and financial determinants of current account deficit in North Macedonia: Evidence from time series," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 42(1), pages 65-94.
    40. Carlomagno, Guillermo, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    41. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    42. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
    43. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    44. Weigand Roland & Wanger Susanne & Zapf Ines, 2018. "Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany," Journal of Official Statistics, Sciendo, vol. 34(1), pages 265-301, March.
    45. Corona, Francisco & Orraca, Pedro, 2016. "Remittances in Mexico and their unobserved components," DES - Working Papers. Statistics and Econometrics. WS 22674, Universidad Carlos III de Madrid. Departamento de Estadística.
    46. Kyle E. Binder & Mohsen Pourahmadi & James W. Mjelde, 2020. "The role of temporal dependence in factor selection and forecasting oil prices," Empirical Economics, Springer, vol. 58(3), pages 1185-1223, March.
    47. Pallara, Kevin, 2016. "The dynamic effects of government spending: a FAVAR approach," MPRA Paper 92283, University Library of Munich, Germany.
    48. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    49. Dedu, Vasile & Stoica, Tiberiu, 2014. "The Impact of Monetaru Policy on the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 71-86, June.
    50. Christophe Bellégo & Laurent Ferrara, 2010. "A factor-augmented probit model for business cycle analysis," EconomiX Working Papers 2010-14, University of Paris Nanterre, EconomiX.
    51. Masud Alam, 2024. "Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach," Empirical Economics, Springer, vol. 67(4), pages 1421-1471, October.
    52. Daoui Marouane, 2023. "Macroeconomic Forecasting using Dynamic Factor Models: The Case of Morocco," Papers 2302.14180, arXiv.org, revised May 2023.
    53. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2020. "Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors," Econometrics, MDPI, vol. 8(1), pages 1-23, February.
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    55. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    56. Tobias Hartl, 2020. "Macroeconomic Forecasting with Fractional Factor Models," Papers 2005.04897, arXiv.org.
    57. Barhoumi, K. & Darn , O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    58. Stoupos, Nikolaos & Nikas, Christos & Kiohos, Apostolos, 2023. "Turkey: From a thriving economic past towards a rugged future? - An empirical analysis on the Turkish financial markets," Emerging Markets Review, Elsevier, vol. 54(C).

  82. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.

    Cited by:

    1. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    2. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    3. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    4. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    5. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    6. Bell, Venetia & Co, Lai Wah & Stone, Sophie & Wallis, gavin`, 2014. "Nowcasting UK GDP growth," Bank of England Quarterly Bulletin, Bank of England, vol. 54(1), pages 58-68.
    7. Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter, 2014. "Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components," Tinbergen Institute Discussion Papers 14-113/III, Tinbergen Institute.
    8. Henzel Steffen R. & Lehmann Robert & Wohlrabe Klaus, 2015. "Nowcasting Regional GDP: The Case of the Free State of Saxony," Review of Economics, De Gruyter, vol. 66(1), pages 71-98, April.
    9. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    10. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    11. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    12. Winkelried, Diego, 2012. "Predicting quarterly aggregates with monthly indicators," Working Papers 2012-023, Banco Central de Reserva del Perú.
    13. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    14. Luci Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon M. Potter, 2014. "Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences," Staff Reports 680, Federal Reserve Bank of New York.
    15. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    16. Özer Karagedikli & Murat Özbilgin, 2019. "Mixed in New Zealand: Nowcasting Labour Markets with MIDAS," Reserve Bank of New Zealand Analytical Notes series AN2019/04, Reserve Bank of New Zealand.
    17. Michael Funke & Aaron Mehrotra & Hao Yu, 2015. "Tracking Chinese CPI inflation in real time," Empirical Economics, Springer, vol. 48(4), pages 1619-1641, June.
    18. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    19. Schumacher, Christian & Marcellino, Massimiliano & Foroni, Claudia, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.
    20. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    21. Katja Drechsel & Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    22. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    23. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    24. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    25. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023. "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers 1385, Board of Governors of the Federal Reserve System (U.S.).
    26. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    27. Anastasia Mogilat & Oleg Kryzhanovskiy & Zhanna Shuvalova & Yaroslav Murashov, 2024. "DYFARUS: Dynamic Factor Model to Forecast GDP by Output Using Input-Output Tables," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 3-25, June.
    28. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    29. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    30. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
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    165. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    166. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    167. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    168. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    169. Karlsson, Sune & Mazur, Stepan & Raftab, Mariya, 2025. "Identifying Useful Indicators for Nowcasting GDP in Sweden," Working Papers 2025:4, Örebro University, School of Business.
    170. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    171. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    172. Guay, Alain & Maurin, Alain, 2015. "Disaggregation methods based on MIDAS regression," Economic Modelling, Elsevier, vol. 50(C), pages 123-129.
    173. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    174. Cláudia Duarte & Sónia Cabral, 2016. "Nowcasting Portuguese tourism exports," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    175. Trujillo-Barrera, Andres & Pennings, Joost M.E., 2013. "Energy and Food Commodity Prices Linkage: An Examination with Mixed-Frequency Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150465, Agricultural and Applied Economics Association.
    176. Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.
    177. Yun-Yeong Kim, 2016. "Dynamic Analyses Using VAR Model with Mixed Frequency Data through Observable Representation," Korean Economic Review, Korean Economic Association, vol. 32, pages 41-75.

  83. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "Pooling versus model selection for nowcasting with many predictors: An application to German GDP," CEPR Discussion Papers 7197, C.E.P.R. Discussion Papers.

    Cited by:

    1. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
    2. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    3. Katja Drechsel & Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    5. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    6. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    7. Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    8. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    9. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    10. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
    11. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    12. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    13. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    14. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    15. Marcus Scheiblecker, 2010. "Can the Inclusion of Calendar and Temperature Effects Improve Nowcasts and Forecasts of Construction Sector Output Based on Business Surveys?," WIFO Working Papers 374, WIFO.
    16. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    17. Alejo Estavillo & Gabriela Mordecki, 2023. "Nowcasting del PIB para Uruguay en base a un modelo de ecuaciones puente," Documentos de Trabajo (working papers) 23-26, Instituto de Economía - IECON.
    18. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    19. Drechsel, Katja & Scheufele, Rolf, 2011. "The Financial Crisis from a Forecaster’s Perspective," IWH Discussion Papers 5/2011, Halle Institute for Economic Research (IWH).
    20. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    21. Abdić Ademir & Resić Emina & Abdić Adem & Rovčanin Adnan, 2020. "Nowcasting GDP of Bosnia and Herzegovina: A Comparison of Forecast Accuracy Models," South East European Journal of Economics and Business, Sciendo, vol. 15(2), pages 1-14, December.
    22. Lenza Michele & Warmedinger Thomas, 2011. "A Factor Model for Euro-area Short-term Inflation Analysis," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 50-62, February.
    23. Philipp an de Meulen & Martin Micheli & Torsten Schmidt, 2014. "Forecasting real estate prices in Germany: the role of consumer confidence," Journal of Property Research, Taylor & Francis Journals, vol. 31(3), pages 244-263, September.
    24. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.

  84. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.

    Cited by:

    1. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
    2. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    3. Wenzel, Lars & Wolf, André, 2013. "Short-term forecasting with business surveys: Evidence for German IHK data at federal state level," HWWI Research Papers 140, Hamburg Institute of International Economics (HWWI).
    4. Franco, Ray John Gabriel & Mapa, Dennis S., 2014. "The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach," MPRA Paper 55858, University Library of Munich, Germany.
    5. Hopp Daniel, 2022. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Journal of Official Statistics, Sciendo, vol. 38(3), pages 847-873, September.
    6. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    7. Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    8. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    9. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    10. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    11. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    12. Daniel Hopp, 2021. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers 2106.08901, arXiv.org.
    13. Neville Francis & Eric Ghysels & Michael T. Owyang, 2011. "The low-frequency impact of daily monetary policy shocks," Working Papers 2011-009, Federal Reserve Bank of St. Louis.
    14. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    15. Marozzi, Armando, 2021. "The ECB's tracker: nowcasting the press conferences of the ECB," Working Paper Series 2609, European Central Bank.
    16. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    17. Yun-Yeong Kim, 2016. "Dynamic Analyses Using VAR Model with Mixed Frequency Data through Observable Representation," Korean Economic Review, Korean Economic Association, vol. 32, pages 41-75.

  85. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).

    Cited by:

    1. Carlos Carvalho & Jae Won Lee, 2011. "Sectoral Price Facts in a Sticky-Price Model," Departmental Working Papers 201133, Rutgers University, Department of Economics.
    2. Antoni Espasa & Eva Senra, 2017. "Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis," Econometrics, MDPI, vol. 5(4), pages 1-28, October.

  86. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.

    Cited by:

    1. Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo.
    2. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    3. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    4. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    5. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    6. Gianluca Cubadda & Marco Mazzali, 2024. "The vector error correction index model: representation, estimation and identification," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 126-150.
    7. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    8. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    9. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    10. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    11. Churm, Rohan & Joyce, Mike & Kapetanios, George & Theodoridis, Konstantinos, 2015. "Unconventional monetary policies and the macroeconomy: the impact of the United Kingdom's QE2 and Funding for Lending Scheme," Bank of England working papers 542, Bank of England.
    12. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    13. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    14. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    15. Marco Centoni & Gianluca Cubadda, 2015. "Common Feature Analysis of Economic Time Series: An Overview and Recent Developments," CEIS Research Paper 355, Tor Vergata University, CEIS, revised 05 Oct 2015.
    16. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    17. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Common drifting volatility in large Bayesian VARs," Working Papers (Old Series) 1206, Federal Reserve Bank of Cleveland.
    18. Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Adaptive hierarchical priors for high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 212(1), pages 241-271.
    19. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.
    20. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    21. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    22. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
    23. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.
    24. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    25. Bernardini, Emmanuela & Cubadda, Gianluca, 2015. "Macroeconomic forecasting and structural analysis through regularized reduced-rank regression," International Journal of Forecasting, Elsevier, vol. 31(3), pages 682-691.
    26. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working papers 2011-02, University of Connecticut, Department of Economics, revised Aug 2012.
    27. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    28. Justyna Wróblewska & Anna Pajor, 2019. "One-period joint forecasts of Polish inflation, unemployment and interest rate using Bayesian VEC-MSF models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 11(1), pages 23-45, March.
    29. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    30. Matilainen, M. & Croux, C. & Nordhausen, K. & Oja, H., 2017. "Supervised dimension reduction for multivariate time series," Econometrics and Statistics, Elsevier, vol. 4(C), pages 57-69.
    31. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    32. Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    33. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    34. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    35. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    36. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
    37. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    38. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    39. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    40. Joshua C.C. Chan & Eric Eisenstat & Rodney W. Strachan, 2018. "Reducing dimensions in a large TVP-VAR," CAMA Working Papers 2018-49, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    41. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    42. Bin Jiang & George Athanasopoulos & Rob J Hyndman & Anastasios Panagiotelis & Farshid Vahid, 2017. "Macroeconomic forecasting for Australia using a large number of predictors," Monash Econometrics and Business Statistics Working Papers 2/17, Monash University, Department of Econometrics and Business Statistics.
    43. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2018. "The global component of inflation volatility," Temi di discussione (Economic working papers) 1170, Bank of Italy, Economic Research and International Relations Area.
    44. Lombardi, Marco J. & Osbat, Chiara & Schnatz, Bernd, 2010. "Global commodity cycles and linkages a FAVAR approach," Working Paper Series 1170, European Central Bank.
    45. Pirschel, Inske & Wolters, Maik, 2014. "Forecasting German key macroeconomic variables using large dataset methods," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100587, Verein für Socialpolitik / German Economic Association.
    46. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
    47. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    48. Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021. "Forecasting the production side of GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
    49. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.
    50. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
    51. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    52. Bin Jiang & Anastasios Panagiotelis & George Athanasopoulos & Rob Hyndman & Farshid Vahid, 2016. "Bayesian Rank Selection in Multivariate Regression," Monash Econometrics and Business Statistics Working Papers 6/16, Monash University, Department of Econometrics and Business Statistics.
    53. Gianluca Cubadda, 2024. "VAR models with an index structure: A survey with new results," Papers 2412.11278, arXiv.org.
    54. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
    55. Zeng, Jing, 2014. "Forecasting Aggregates with Disaggregate Variables: Does boosting help to select the most informative predictors?," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100310, Verein für Socialpolitik / German Economic Association.
    56. Prüser, Jan, 2023. "Data-based priors for vector error correction models," International Journal of Forecasting, Elsevier, vol. 39(1), pages 209-227.
    57. Jing Zeng, 2014. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Working Paper Series of the Department of Economics, University of Konstanz 2014-20, Department of Economics, University of Konstanz.
    58. Churm, Rohan & Joyce, Michael & Kapetanios, George & Theodoridis, Konstantinos, 2021. "Unconventional monetary policies and the macroeconomy: The impact of the UK's QE2 and funding for lending scheme," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 721-736.
    59. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    60. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.

  87. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2009. "Survey Data as Coicident or Leading Indicators," Economics Working Papers ECO2009/19, European University Institute.

    Cited by:

    1. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, May.
    2. Cecilia Frale, Serena Teobaldo, Marco Cacciotti, Alessandra Caretta, 2013. "A Quarterly Measure Of Potential Output In The New European Fiscal Framework," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    3. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    4. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    5. Bialowolski, Piotr & Kuszewski, Tomasz & Witkowski, Bartosz, 2015. "Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-37.
    6. Sieds, 2013. "Complete Volume LXVII n.2 2013," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 1-197, April-Jun.
    7. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    8. Deimante Teresiene & Greta Keliuotyte-Staniuleniene & Yiyi Liao & Rasa Kanapickiene & Ruihui Pu & Siyan Hu & Xiao-Guang Yue, 2021. "The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators," JRFM, MDPI, vol. 14(4), pages 1-23, April.
    9. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    10. Marco Cacciotti & Cecilia Frale & Serena Teobaldo, 2013. "A new methodology for a quarterly measure of the output gap," Working Papers 6, Department of the Treasury, Ministry of the Economy and of Finance.
    11. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
    12. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    13. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    14. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    15. Moauro, Filippo, 2010. "A monthly indicator of employment in the euro area: real time analysis of indirect estimates," MPRA Paper 27797, University Library of Munich, Germany, revised 30 Dec 2010.
    16. Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ifo Working Paper Series 196, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    17. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    18. Pérez-Quirós, Gabriel & Camacho, Máximo, 2009. "Introducing the Euro-STING: Short-Term Indicator of Euro Area Growth," CEPR Discussion Papers 7343, C.E.P.R. Discussion Papers.
    19. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    20. Daniel Kaufmann & Rolf Scheufele, 2015. "Business tendency surveys and macroeconomic fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
    21. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    22. Bhattacharya, Rudrani & Pandey, Radhika & Veronese, Giovanni, 2011. "Tracking India Growth in Real Time," Working Papers 11/90, National Institute of Public Finance and Policy.
    23. Daniel Roash & Tanya Suhoy, 2019. "Sentiment Indicators Based on a Short Business Tendency Survey," Bank of Israel Working Papers 2019.11, Bank of Israel.
    24. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    25. Sandra Hanslin Grossmann & Rolf Scheufele, 2016. "Foreign PMIs: A reliable indicator for exports?," Working Papers 2016-01, Swiss National Bank.
    26. Chrystalleni Aristidou & Kevin Lee & Kalvinder Shields, 2015. "Real-Time Data should be used in Forecasting Output Growth and Recessionary Events in the US," Discussion Papers 2015/13, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    27. Alain Galli, 2018. "Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 179-218, November.
    28. Camila Figueroa S. & Michael Pedersen, 2019. "Extracting information on economic activity from business and consumer surveys in an emerging economy (Chile)," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(3), pages 098-131, December.
    29. Ramazan Yanik & Asfia Binte Osman & Ozcan Ozturk, 2020. "Impact of manufacturing PMI on stock market index: A study on Turkey," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 6(3), pages 104-108.
    30. Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.
    31. Anna Norin, 2011. "Nowcasting of the Gross Regional Product," ERSA conference papers ersa10p768, European Regional Science Association.
    32. Barhoumi, K. & Darn , O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    33. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.

  88. Schumacher, Christian & Marcellino, Massimiliano, 2008. "Factor-MIDAS for now- and forecasting with ragged-edge data: A model comparison for German GDP," CEPR Discussion Papers 6708, C.E.P.R. Discussion Papers.

    Cited by:

    1. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    2. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    3. Laurent Ferrara & Clément Marsilli, 2012. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," Working Papers hal-04141077, HAL.
    4. Dominique Guégan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Economics Bulletin, AccessEcon, vol. 30(1), pages 508-518.
    5. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2010. "Forecasting with Factor-augmented Error Correction Models," CEPR Discussion Papers 7677, C.E.P.R. Discussion Papers.
    6. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    8. Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta, 2010. "Nowcasting," Working Paper Series 1275, European Central Bank.
    9. Bouwman, Kees E. & Jacobs, Jan P.A.M., 2005. "Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions," CCSO Working Papers 200505, University of Groningen, CCSO Centre for Economic Research.
    10. Andrade, P. & Fourel, V. & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    11. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    12. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Post-Print halshs-00460461, HAL.
    13. Bell go, C. & Ferrara, L., 2009. "Forecasting Euro-area recessions using time-varying binary response models for financial," Working papers 259, Banque de France.
    14. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    15. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    16. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
    17. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    18. Guido Bulligan & Massimiliano Marcellino & Fabrizio Venditti, 2012. "Forecasting economic activity with higher frequency targeted predictors," Temi di discussione (Economic working papers) 847, Bank of Italy, Economic Research and International Relations Area.
    19. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    20. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
    21. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Post-Print halshs-00460472, HAL.
    22. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," PSE-Ecole d'économie de Paris (Postprint) halshs-00460461, HAL.
    23. Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.
    24. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    25. Pami Dua, 2023. "Macroeconomic Modelling and Bayesian Methods," Springer Books, in: Pami Dua (ed.), Macroeconometric Methods, chapter 0, pages 19-37, Springer.
    26. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    27. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
    28. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    29. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    30. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    31. Мекенбаева Камила // Mekenbayeva Kamila & Karel Musil, 2017. "Система прогнозирования в Национальном Банке Казахстана: наукаст на основа опросов // Forecasting system at the National Bank of Kazakhstan: survey-based nowcasting," Working Papers #2017-1, National Bank of Kazakhstan.
    32. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2009. "GDP nowcasting with ragged-edge data : A semi-parametric modelling," Post-Print halshs-00344839, HAL.
    33. Lenza Michele & Warmedinger Thomas, 2011. "A Factor Model for Euro-area Short-term Inflation Analysis," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 50-62, February.
    34. Chen, Pu, 2009. "A Note on Updating Forecasts When New Information Arrives between Two Periods," Economics Discussion Papers 2009-22, Kiel Institute for the World Economy (IfW Kiel).
    35. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
    36. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," PSE-Ecole d'économie de Paris (Postprint) halshs-00460472, HAL.
    37. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    38. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    39. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A short note on the nowcasting and the forecasting of Euro-area GDP using non-parametric techniques," Post-Print halshs-00461711, HAL.
    40. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.

  89. Demertzis, Maria & Viegi, Nicola & Marcellino, Massimiliano, 2008. "A Measure for Credibility: Tracking US Monetary Developments," CEPR Discussion Papers 7036, C.E.P.R. Discussion Papers.

    Cited by:

    1. Timo Henckel & Gordon Menzies & Daniel J. Zizzo, 2013. "The Great Recession and the Two Dimensions of European Central Bank Credibility," Working Paper Series 13, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Issler, João Victor & Soares, Ana Flávia, 2019. "Central Bank credibility and inflation expectations: a microfounded forecasting approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 812, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    3. Łyziak, Tomasz & Paloviita, Maritta, 2016. "Anchoring of inflation expectations in the euro area: recent evidence based on survey data," Working Paper Series 1945, European Central Bank.
    4. Carlos Medel, 2018. "Econometric Analysis on Survey-data-based Anchoring of Inflation Expectations in Chile," Working Papers Central Bank of Chile 825, Central Bank of Chile.
    5. Łyziak, Tomasz & Mackiewicz-Łyziak, Joanna, 2020. "Does fiscal stance affect inflation expectations? Evidence for European economies," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 296-310.
    6. Martin Kliem & Alexander Kriwoluzky & Samad Sarferaz, 2016. "On the Low‐Frequency Relationship Between Public Deficits and Inflation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 566-583, April.
    7. Goy, Gavin & Hommes, Cars & Mavromatis, Kostas, 2022. "Forward guidance and the role of central bank credibility under heterogeneous beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1240-1274.
    8. Timo Henckel & Gordon D. Menzies & Peter Moffat & Daniel J. Zizzo, 2019. "Three Dimensions of Central Bank Credibility and Inferential Expectations: The Euro Zone," Working Paper Series 2019/02, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    9. Łyziak, Tomasz & Paloviita, Maritta, 2017. "Formation of inflation expectations in turbulent times: Can ECB manage inflation expectations of professional forecasters?," Bank of Finland Research Discussion Papers 13/2017, Bank of Finland.
    10. Warwick J McKibbin & Augustus J Panton, 2018. "Twenty-five Years of Inflation Targeting in Australia: Are There Better Alternatives for the Next Twenty-five Years?," RBA Annual Conference Volume (Discontinued), in: John Simon & Maxwell Sutton (ed.),Central Bank Frameworks: Evolution or Revolution?, Reserve Bank of Australia.
    11. Malikane, Christopher & Mokoka, Tshepo, 2012. "Monetary policy credibility: A Phillips curve view," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(3), pages 266-271.
    12. Serkan ÇİÇEK & Cüneyt AKAR & Eray YÜCEL, 2011. "Türkiye’de enflasyon beklentilerinin çapalanması ve güvenilirlik," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 26(304), pages 37-55.
    13. Libich Jan, 2011. "Inflation Nutters? Modelling the Flexibility of Inflation Targeting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 11(1), pages 1-36, June.
    14. José Vicente Romero & Sara Naranjo-Saldarriaga, 2022. "Weather Shocks and Inflation Expectations in Semi-Structural Models," Borradores de Economia 1218, Banco de la Republica de Colombia.
    15. Pongsak Luangaram & Yuthana Sethapramote & Chutiorn Tontivanichanon, 2015. "Inflation Expectations and Monetary Policy in Thailand," PIER Discussion Papers 3, Puey Ungphakorn Institute for Economic Research.
    16. Todd E. Clark & Troy Davig, 2008. "An empirical assessment of the relationships among inflation and short- and long-term expectations," Research Working Paper RWP 08-05, Federal Reserve Bank of Kansas City.

  90. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.

    Cited by:

    1. Deeney, Peter & Cummins, Mark & Dowling, Michael & Bermingham, Adam, 2015. "Sentiment in oil markets," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 179-185.
    2. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    3. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    4. Maximo Camacho & Gabriel Perez-Quiros, 2009. "Ñ-STING: España Short Term INdicator of Growth," Working Papers 0912, Banco de España.
    5. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2009. "Survey Data as Coicident or Leading Indicators," Economics Working Papers ECO2009/19, European University Institute.
    6. Elena Angelini & Marta Banbura & Gerhard Rünstler, 2010. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-22.
    7. Liu, Yang & Swanson, Norman R., 2024. "An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1391-1409.
    8. Pérez-Quirós, Gabriel & Camacho, Máximo, 2009. "Introducing the Euro-STING: Short-Term Indicator of Euro Area Growth," CEPR Discussion Papers 7343, C.E.P.R. Discussion Papers.
    9. Tihana Skrinjaric, 2022. "Macroeconomic effects of systemic stress: a rolling spillover index approach," Public Sector Economics, Institute of Public Finance, vol. 46(1), pages 109-140.
    10. Cecilia Frale, "undated". "Do Surveys Help in Macroeconomic Variables Disaggregation and Estimation?," Working Papers wp2008-2, Department of the Treasury, Ministry of the Economy and of Finance.
    11. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.

  91. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "Forecasting with Dynamic Models using Shrinkage-based Estimation," Working Papers 635, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.
    2. Emrah Oral & Gazanfer Unal, 2017. "Co-movement of precious metals and forecasting using scale by scale wavelet transform," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-21, March.

  92. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    2. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    3. Andrade, P. & Fourel, V. & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    4. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
    5. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    6. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," PSE-Ecole d'économie de Paris (Postprint) halshs-00460461, HAL.
    7. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    8. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," PSE-Ecole d'économie de Paris (Postprint) halshs-00460472, HAL.

  93. George Kapetanios & Massimiliano Marcellino, 2008. "Cross-sectional Averaging and Instrumental Variable Estimation with Many Weak Instruments," Working Papers 627, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Malikane, Christopher, 2013. "A New Keynesian Triangle Phillips Curve," MPRA Paper 43548, University Library of Munich, Germany.

  94. Oscar Jorda & Massimiliano Marcellino, 2008. "Path Forecast Evaluation," Working Papers 131, University of California, Davis, Department of Economics.

    Cited by:

    1. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    2. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
    3. Matei Demetrescu & Mu-Chun Wang, 2014. "Incorporating Asymmetric Preferences into Fan Charts and Path Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 287-297, April.
    4. Dag Kolsrud, 2015. "A Time‐Simultaneous Prediction Box for a Multivariate Time Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(8), pages 675-693, December.
    5. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    6. Michael W. McCracken & Joseph McGillicuddy, 2017. "An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts," Working Papers 2017-40, Federal Reserve Bank of St. Louis.
    7. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2013. "Comparison of Methods for Constructing Joint Confidence Bands for Impulse Response Functions," MAGKS Papers on Economics 201325, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Grabowski, Daniel & Staszewska-Bystrova, Anna, 2018. "Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181590, Verein für Socialpolitik / German Economic Association.
    9. Stefan Bruder, 2014. "Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions," ECON - Working Papers 181, Department of Economics - University of Zurich, revised Dec 2015.
    10. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    11. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    12. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2018. "Constructing Joint Confidence Bands for Impulse Response Functions of VAR Models: A Review," Discussion Papers of DIW Berlin 1762, DIW Berlin, German Institute for Economic Research.
    13. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    14. Anna Staszewska‐Bystrova, 2011. "Bootstrap prediction bands for forecast paths from vector autoregressive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(8), pages 721-735, December.
    15. Staszewska-Bystrova Anna, 2013. "Modified Scheffé’s Prediction Bands," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(5-6), pages 680-690, October.
    16. Jeanne Aslak Petersen & Ditte Heide-Jørgensen & Nina Detlefsen & Trine Boomsma, 2016. "Short-term balancing of supply and demand in an electricity system: forecasting and scheduling," Annals of Operations Research, Springer, vol. 238(1), pages 449-473, March.
    17. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    18. Chevillon, Guillaume, 2009. "Multi-step forecasting in emerging economies: An investigation of the South African GDP," International Journal of Forecasting, Elsevier, vol. 25(3), pages 602-628, July.
    19. Schüssler, Rainer & Trede, Mark, 2016. "Constructing minimum-width confidence bands," Economics Letters, Elsevier, vol. 145(C), pages 182-185.
    20. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2014. "Confidence Bands for Impulse Responses: Bonferroni versus Wald," Discussion Papers of DIW Berlin 1354, DIW Berlin, German Institute for Economic Research.
    21. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    22. Jeanne Aslak Petersen & Ditte Mølgård Heide-Jørgensen & Nina Kildegaard Detlefsen & Trine Krogh Boomsma, 2016. "Short-term balancing of supply and demand in an electricity system: forecasting and scheduling," Annals of Operations Research, Springer, vol. 238(1), pages 449-473, March.
    23. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    24. Pinto, Rui & Bessa, Ricardo J. & Matos, Manuel A., 2017. "Multi-period flexibility forecast for low voltage prosumers," Energy, Elsevier, vol. 141(C), pages 2251-2263.
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    Cited by:

    1. Teona Shugliashvili, 2023. "The words have power: the impact of news on exchange rates," FFA Working Papers 5.006, Prague University of Economics and Business, revised 31 Jul 2023.
    2. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    3. Joshua C.C. Chan & Angelia L. Grant, 2016. "Reconciling output gaps: unobserved components model and Hodrick-Prescott filter," CAMA Working Papers 2016-44, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Jonas Dovern & Martin Feldkircher & Florian Huber, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR," Working Papers 200, Oesterreichische Nationalbank (Austrian Central Bank).
    5. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    6. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2020. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 934-943, September.
    7. Rossi, Barbara, 2013. "Exchange Rate Predictability," CEPR Discussion Papers 9575, C.E.P.R. Discussion Papers.
    8. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    9. Costantini, Mauro & Crespo Cuaresma, Jesus & Hlouskova, Jaroslava, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Department of Economics Working Paper Series 176, WU Vienna University of Economics and Business.
    10. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    11. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    12. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    13. Joshua C. C. Chan, 2024. "BVARs and stochastic volatility," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 3, pages 43-67, Edward Elgar Publishing.
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    20. Koop, Gary & Korobilis, Dimitris, 2012. "Large time-varying parameter VARs," MPRA Paper 38591, University Library of Munich, Germany.
    21. Xin Sheng & Rangan Gupta & Afees A. Salisu & Elie Bouri, 2021. "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Working Papers 202101, University of Pretoria, Department of Economics.
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    24. Abdul-Rashid Abdul-Rahaman & Coleman Martha & Emmanuel Caesar Ayamba, 2024. "Exchange Rate Models and the Management of Forex Losses in Ghana: Modelling Exchange Rate Volatilities for Businesses," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 49(4), pages 679-703, November.
    25. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    26. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    27. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
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    30. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    31. Huber, Florian & Krisztin, Tamás & Piribauer, Philipp, 2014. "Forecasting Global Equity Indices Using Large Bayesian VARs," Department of Economics Working Paper Series 184, WU Vienna University of Economics and Business.
    32. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2018. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2018-08, Department of Economics, University of Konstanz.
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    34. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    35. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
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    42. Dovern, Jonas & Feldkircher, Martin & Huber, Florian, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Multivariate Forecasts from a Bayesian GVAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112999, Verein für Socialpolitik / German Economic Association.
    43. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
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    45. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    46. Ms. Adina Popescu & Ms. Alina Carare, 2011. "Monetary Policy and Risk-Premium Shocks in Hungary: Results from a Large Bayesian VAR," IMF Working Papers 2011/259, International Monetary Fund.
    47. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    48. Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2016. "Testing for Instability in Covariance Structures," Working papers 2016-33, University of Connecticut, Department of Economics.
    49. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    50. Jesus Crespo Cuaresma & Ines Fortin & Jaroslava Hlouskova, 2018. "Exchange rate forecasting and the performance of currency portfolios," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 519-540, August.
    51. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    52. Florian Huber, 2014. "Density Forecasting using Bayesian Global Vector Autoregressions with Common Stochastic Volatility," Department of Economics Working Papers wuwp179, Vienna University of Economics and Business, Department of Economics.
    53. Papahristodoulou, Christos, 2019. "Is there any theory that explains the SEK?," MPRA Paper 95072, University Library of Munich, Germany, revised 08 Jul 2019.
    54. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    55. Florian Huber & Jesus Crespo-Cuaresma & Martin Feldkircher, 2014. "Forecasting with Bayesian Global Vector Autoregressions," ERSA conference papers ersa14p25, European Regional Science Association.
    56. Florian Huber & Gary Koop & Massimiliano Marcellino & Tobias Scheckel, 2024. "Bayesian modelling of VAR precision matrices using stochastic block networks," Papers 2407.16349, arXiv.org.
    57. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    58. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    59. Marcellino, Massimiliano & Abbate, Angela, 2016. "Point, interval and density forecasts of exchange rates with time-varying parameter models," CEPR Discussion Papers 11559, C.E.P.R. Discussion Papers.
    60. Bacchetta, Philippe & van Wincoop, Eric & Beutler, Toni, 2009. "Can Parameter Instability Explain the Meese-Rogoff Puzzle?," CEPR Discussion Papers 7383, C.E.P.R. Discussion Papers.
    61. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    62. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, May.
    63. Kim, Young Min & Lee, Seojin, 2020. "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 117-134.
    64. Brian D. Deaton, 2018. "Effects of the Swiss Franc/Euro Exchange Rate Floor on the Calibration of Probability Forecasts," Forecasting, MDPI, vol. 1(1), pages 1-23, May.
    65. Brahim Gaies, Khaled Guesmi, Thomas Porcher, Raphael Boroumand, 2020. "Financial instability and oil price fluctuations: evidence from oil exporting developing countries," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 17(1), pages 55-71, June.
    66. Auer, Simone, 2019. "Monetary policy shocks and foreign investment income: Evidence from a large Bayesian VAR," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 142-166.
    67. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.
    68. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
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    70. Antonio Pacifico, 2022. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure," Econometrics, MDPI, vol. 10(3), pages 1-24, July.
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    72. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    73. Korobilis, Dimitris, 2016. "Prior selection for panel vector autoregressions," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 110-120.
    74. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
    75. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
    76. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.
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    Cited by:

    1. Brandon J. Bates & Mikkel Plagborg-Møller & James H. Stock & Mark W. Watson, "undated". "Consistent factor estimation in dynamic factor models with structural instability," Working Paper 84631, Harvard University OpenScholar.
    2. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," BOFIT Discussion Papers 12/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    3. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    4. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate Pass-Through in the Major European Economies - The Role of Expectations," Discussion Papers 10/03, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    5. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    6. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2010. "Forecasting with Factor-augmented Error Correction Models," CEPR Discussion Papers 7677, C.E.P.R. Discussion Papers.
    7. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    8. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    9. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    10. McNown, Robert & Seip, Knut Lehre, 2011. "Periods and structural breaks in US economic history 1959-2007," Journal of Policy Modeling, Elsevier, vol. 33(2), pages 169-182, March.
    11. Han, Xu & Inoue, Atsushi, 2015. "Tests For Parameter Instability In Dynamic Factor Models," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1117-1152, October.
    12. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    13. Liang Chen & Juan Dolado & Jesus Gonzalo, 2013. "Detecting Big Structural Breaks in Large Factor Models," Economics Series Working Papers 677, University of Oxford, Department of Economics.
    14. Badi H. Baltagi & Chihwa Kao & Fa Wang, 2016. "The Identification and Estimation of a Large Factor Model with Structural Instability," Center for Policy Research Working Papers 194, Center for Policy Research, Maxwell School, Syracuse University.
    15. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    16. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    17. Gloria Gonzalez-Rivera & Esther Ruiz & Javier Vicente, 2018. "Growth in Stress," Working Papers 201805, University of California at Riverside, Department of Economics.
    18. Fu, Zhonghao & Hong, Yongmiao & Wang, Xia, 2023. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," Journal of Econometrics, Elsevier, vol. 233(1), pages 302-331.
    19. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    20. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2016. "Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1254-1275, November.
    21. Lopez-Buenache, German, 2019. "The evolution of monetary policy effectiveness under macroeconomic instability," Economic Modelling, Elsevier, vol. 83(C), pages 221-233.
    22. Nivín, Rafael & Pérez, Fernando, 2019. "Estimación de un Índice de Condiciones Financieras para el Perú," Working Papers 2019-006, Banco Central de Reserva del Perú.
    23. Boniface Yemba & Yi Duan & Nabaneeta Biswas, 2023. "Government spending news and stock price index," Economics Bulletin, AccessEcon, vol. 43(4), pages 1816-1841.
    24. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
    25. Olga Bondarenko, 2020. "The Missing “Cycle” Part and Other Thoughts on the Global Financial Cycle," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 250, pages 15-32.
    26. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    27. Breitung, Jörg & Eickmeier, Sandra, 2009. "Testing for structural breaks in dynamic factor models," Discussion Paper Series 1: Economic Studies 2009,05, Deutsche Bundesbank.
    28. Gary Koop & Dimitris Korobilis, 2013. "A new index of financial conditions," Working Papers 1307, University of Strathclyde Business School, Department of Economics.
    29. Badi H. Baltagi & Qu Feng & Chihwa Kao, 2015. "Estimation of Heterogeneous Panels with Structural Breaks," Center for Policy Research Working Papers 179, Center for Policy Research, Maxwell School, Syracuse University.
    30. Mr. Luis Brandão-Marques & Mrs. Esther Perez Ruiz, 2017. "How Financial Conditions Matter Differently across Latin America," IMF Working Papers 2017/218, International Monetary Fund.
    31. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    32. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank.
    33. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.
    34. Bystrov, Victor & di Salvatore, Antonietta, 2013. "Martingale approximation of eigenvalues for common factor representation," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 233-237.
    35. Eraslan, Sercan & Schröder, Maximilian, 2019. "Nowcasting GDP with a large factor model space," Discussion Papers 41/2019, Deutsche Bundesbank.
    36. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper series 53_12, Rimini Centre for Economic Analysis.
    37. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
    38. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    39. Germán López, 2015. "Forecast Accuracy of Small and Large Scale Dynamic Factor Models in Developing Economies," Working Papers. Serie AD 2015-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    40. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    41. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    42. MeiChi Huang, 2019. "A Nationwide or Localized Housing Crisis? Evidence from Structural Instability in US Housing Price and Volume Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1547-1563, April.
    43. Urga, Giovanni & Wang, Fa, 2022. "Estimation and Inference for High Dimensional Factor Model with Regime Switching," MPRA Paper 117012, University Library of Munich, Germany, revised 10 Apr 2023.
    44. Bueno, José Luis Cendejas & Santos, Sonia de Lucas & Rodríguez, M Jesús Delgado & Ayuso, Inmaculada Álvarez, 2011. "Testing for structural breaks in factor loadings: An application to international business cycle," Economic Modelling, Elsevier, vol. 28(1-2), pages 259-263, January.
    45. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
    46. Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
    47. Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2015. "Classical time varying factor-augmented vector auto-regressive models—estimation, forecasting and structural analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 493-533, June.
    48. Urga, Giovanni & Wang, Fa, 2022. "Estimation and inference for high dimensional factor model with regime switching," MPRA Paper 113172, University Library of Munich, Germany.
    49. Yanhong Feng & Dilong Xu & Pierre Failler & Tinghui Li, 2020. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation," Sustainability, MDPI, vol. 12(16), pages 1-24, August.
    50. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.
    51. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
    52. Elsayed, Ahmed H. & Sousa, Ricardo M., 2024. "International monetary policy and cryptocurrency markets: dynamic and spillover effects," LSE Research Online Documents on Economics 115305, London School of Economics and Political Science, LSE Library.
    53. Alain Kabundi & Asithandile Mbelu, 2021. "Estimating a time-varying financial conditions index for South Africa," Empirical Economics, Springer, vol. 60(4), pages 1817-1844, April.
    54. Urga, Giovanni & Wang, Fa, 2024. "Estimation and inference for high dimensional factor model with regime switching," Journal of Econometrics, Elsevier, vol. 241(2).
    55. Davor Kunovac, 2007. "Factor Model Forecasting of Inflation in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 31(4), pages 371-393.
    56. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    57. Ma, Chenchen & Tu, Yundong, 2023. "Group fused Lasso for large factor models with multiple structural breaks," Journal of Econometrics, Elsevier, vol. 233(1), pages 132-154.

  97. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Branimir, Jovanovic & Magdalena, Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," MPRA Paper 43162, University Library of Munich, Germany.
    2. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    3. Branimir Jovanovic & Magdalena Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," Working Papers 2010-02, National Bank of the Republic of North Macedonia, revised Aug 2010.

  98. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2007. "Factor Analysis in a Model with Rational Expectations," NBER Working Papers 13404, National Bureau of Economic Research, Inc.

    Cited by:

    1. Giovanni Angelini & Luca Fanelli Fanelli, 2015. "Misspecification and Expectations Correction in New Keynesian DSGE Models," Quaderni di Dipartimento 1, Department of Statistics, University of Bologna.
    2. Milda Norkute, 2015. "Can the sectoral New Keynesian Phillips curve explain inflation dynamics in the Euro Area?," Empirical Economics, Springer, vol. 49(4), pages 1191-1216, December.
    3. Giesen, Sebastian & Scheufele, Rolf, 2016. "Effects of incorrect specification on the finite sample properties of full and limited information estimators in DSGE models," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 1-18.
    4. Gary Koop & M. Hashem Pesaran & Ron P. Smith, 2011. "On Identification of Bayesian DSGE Models," CESifo Working Paper Series 3423, CESifo.
    5. Scheufele, Rolf, 2008. "Evaluating the German (New Keynesian) Phillips Curve," IWH Discussion Papers 10/2008, Halle Institute for Economic Research (IWH).
    6. Mavroeidis, Sophocles & Plagborg-Moller, Mikkel & Stock, James H., 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Scholarly Articles 22795845, Harvard University Department of Economics.
    7. Lanne, Markku & Luoto, Jani, 2011. "Autoregression-Based Estimation of the New Keynesian Phillips Curve," MPRA Paper 29801, University Library of Munich, Germany.
    8. Dees, S. & Pesaran, M.H. & Smith, L.V. & Smith, R.P., 2008. "Identification of New Keynesian Phillips Curves from a Global Perspective," Cambridge Working Papers in Economics 0803, Faculty of Economics, University of Cambridge.
    9. Harun Mirza & Lidia Storjohann, 2014. "Making Weak Instrument Sets Stronger: Factor‐Based Estimation of Inflation Dynamics and a Monetary Policy Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 643-664, June.
    10. Milda Norkute & Joakim Westerlund, 2024. "A Factor‐Augmented New Keynesian Phillips Curve for the European Union Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(4), pages 794-810, August.
    11. Norkute, Milda, 2013. "Assessing the New Keynesian Phillips Curve in the Euro Area Using Disaggregate Data," Working Papers 2013:31, Lund University, Department of Economics.

  99. Angelini, Elena & Marcellino, Massimiliano, 2007. "Econometric analyses with backdated data: unified Germany and the euro area," Working Paper Series 752, European Central Bank.

    Cited by:

    1. Federica Ciocchetta & Wanda Cornacchia, 2019. "Assessing financial stability risks from the real estate market in Italy: an update," Questioni di Economia e Finanza (Occasional Papers) 493, Bank of Italy, Economic Research and International Relations Area.
    2. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    3. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
    4. Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.

  100. Andrea Carriero & Massimiliano Marcellino, 2007. "A Comparison of Methods for the Construction of Composite Coincident and Leading Indexes for the UK," Working Papers 590, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    2. Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
    3. Heilemann, Ullrich & Stekler, Herman, 2007. "Introduction to "The future of macroeconomic forecasting"," International Journal of Forecasting, Elsevier, vol. 23(2), pages 159-165.
    4. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.
    5. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    6. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    7. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    8. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Shirly Siew-Ling WONG & Chin-Hong PUAH & Shazali ABU MANSOR & Venus Khim-Sen LIEW, 2016. "Measuring Business Cycle Fluctuations: An Alternative Precursor To Economic Crises," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 235-248.
    10. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    11. Fichtner, Ferdinand & Rüffer, Rasmus & Schnatz, Bernd, 2009. "Leading indicators in a globalised world," Working Paper Series 1125, European Central Bank.
    12. Schnatz, Bernd & D'Agostino, Antonello, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series 1455, European Central Bank.
    13. Heij, C., 2007. "Improved forecasting with leading indicators: the principal covariate index," Econometric Institute Research Papers EI 2007-23, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

  101. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.

  102. Andrea Carriero & Massimiliano Marcellino, 2007. "Sectoral Survey-based Confidence Indicators for Europe," Working Papers 320, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    2. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.
    3. Christian Seiler, 2014. "Mode Preferences in Business Surveys: Evidence from Germany," ifo Working Paper Series 193, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    4. Nicoletta Pashourtidou & Andreas Tsiaklis, 2011. "An Analysis of Firms’ Expectations about Activity and Employment," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 5(1), pages 71-85, June.
    5. Thomas Lux & Duc Thi Luu & Boyan Yanovski, 2020. "An analysis of systemic risk in worldwide economic sentiment indices," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 909-928, November.
    6. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Daniel Kaufmann & Rolf Scheufele, 2015. "Business tendency surveys and macroeconomic fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
    8. Luu, Duc Thi & Yanovski, Boyan & Lux, Thomas, 2018. "An analysis of systematic risk in worldwide econonomic sentiment indices," Economics Working Papers 2018-03, Christian-Albrechts-University of Kiel, Department of Economics.
    9. Camila Figueroa S. & Michael Pedersen, 2019. "Extracting information on economic activity from business and consumer surveys in an emerging economy (Chile)," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(3), pages 098-131, December.
    10. Willem Vanlaer & Samantha Bielen & Wim Marneffe, 2020. "Consumer Confidence and Household Saving Behaviors: A Cross-Country Empirical Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 677-721, January.

  103. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.

    Cited by:

    1. Nagayasu, Jun, 2014. "Regional inflation, spatial location and the Balassa-Samuelson effect," MPRA Paper 59220, University Library of Munich, Germany.
    2. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    3. Ulrich Fritsche & Vladimir Kuzin, 2007. "Unit Labor Cost Growth Differentials in the Euro Area, Germany, and the US: Lessons from PANIC and Cluster Analysis," Discussion Papers of DIW Berlin 667, DIW Berlin, German Institute for Economic Research.
    4. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    5. Zsuzsanna Zsibók & Balázs Varga, 2012. "Inflation Persistence in Hungary: a Spatial Analysis," Working Papers 1203, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    6. Andrea Vaona & Guido Ascari, 2012. "Regional Inflation Persistence: Evidence from Italy," Regional Studies, Taylor & Francis Journals, vol. 46(4), pages 509-523, June.
    7. Giray GOZGOR, 2013. "Unemployment Persistence and Inflation Convergence: Evidence from Regions of Turkey," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 13(1), pages 55-64.
    8. Riccardo Cristadoro & Giuseppe Saporito & Fabrizio Venditti, 2013. "Forecasting inflation and tracking monetary policy in the euro area: does national information help?," Empirical Economics, Springer, vol. 44(3), pages 1065-1086, June.
    9. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2011. "On the importance of sectoral and regional shocks for price-setting," Working Paper Series 1334, European Central Bank.
    10. Giulio Palomba & Emma Sarno & Alberto Zazzaro, 2009. "Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro," Empirical Economics, Springer, vol. 37(2), pages 231-270, October.
    11. Diego Winkelried & José Enrique Gutierrez, 2015. "Regional Inflation Dynamics and Inflation Targeting. The Case of Peru," Journal of Applied Economics, Taylor & Francis Journals, vol. 18(2), pages 199-224, November.
    12. Sebastian Dullien & Ulrich Fritsche, 2008. "Does the dispersion of unit labor cost dynamics in the EMU imply long-run divergence?," International Economics and Economic Policy, Springer, vol. 5(3), pages 269-295, November.
    13. Helena Marques & Gabriel Pino & J.D.Tena, 2009. "Regional inflation dynamics using space-time models," DEA Working Papers 40, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    14. Yanli LI, Hongfeng PENG & Hongfeng PENG, 2013. "Inflation Persistence in Nine Latin American Countries: Panel SURKSS Test with a Fourier Function," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 132-143, October.
    15. Kai Carstensen & Jan Hagen & Oliver Hossfeld & Abelardo Salazar Neaves, 2008. "Money Demand Stability and Inflation Prediction in the Four Largest EMU Countries," ifo Working Paper Series 61, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    16. Perevyshin, Yury (Перевышин, Юрий) & Sinelnikov-Murylev, Sergei Germanovich (Синельников-Мурылев, Сергей Германович) & Skrobotov, Anton (Скроботов, Антон) & Trunin, Pavel (Трунин, Павел), 2018. "Analysis of Regional Price Differentiations [Анализ Региональной Дифференциации Цен]," Published Papers 011801, Russian Presidential Academy of National Economy and Public Administration.
    17. Schnatz, Bernd, 2006. "Is reversion to PPP in euro exchange rates non-linear?," Working Paper Series 682, European Central Bank.
    18. Angelos Liontakis & Dimitris Kremmydas, 2013. "Food Inflation in EU: Distribution Analysis and Spatial Effects," Working Papers 2013-3, Agricultural University of Athens, Department Of Agricultural Economics.
    19. Eric Girardin & Cheikh A. T. Sall, 2018. "Inflation Dynamics of Franc-Zone Countries Determinants, Co-movements and Spatial Interactions," Open Economies Review, Springer, vol. 29(2), pages 295-320, April.
    20. Rüffer, Rasmus & Sánchez, Marcelo & Shen, Jian-Guang, 2007. "Emerging Asia's growth and integration: how autonomous are business cycles?," Working Paper Series 715, European Central Bank.
    21. Sandra Eickmeier, 2009. "Comovements and heterogeneity in the euro area analyzed in a non-stationary dynamic factor model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 933-959.
    22. Serhan Cevik, 2023. "Mind the gap: city-level inflation synchronization," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 70(1), pages 121-139, March.
    23. Carla Massidda & Paolo Mattana, 2008. "Regional productivity and relative prices dynamics: the case of Italy," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(4), pages 945-966, December.
    24. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).
    25. Angelos Liontakis & Christos T. Papadas, 2010. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," Working Papers 2010-6, Agricultural University of Athens, Department Of Agricultural Economics.
    26. George Kapetanios & Massimiliano Marcellino, 2006. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," Working Papers 577, Queen Mary University of London, School of Economics and Finance.
    27. Jörg Breitung & Sandra Eickmeier, 2014. "Analyzing business and financial cycles using multi-level factor models," CAMA Working Papers 2014-43, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    28. Liontakis, Angelos E. & Papadas, Christos T., 2009. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," 113th Seminar, September 3-6, 2009, Chania, Crete, Greece 58084, European Association of Agricultural Economists.
    29. Stefano IACUS & Giuseppe PORRO, 2013. "Does European Monetary Union make inflation dynamics more uniform?," Departmental Working Papers 2013-12, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    30. Aleksandra Halka & Grzegorz Szafranski, 2018. "What Common Factors are Driving Inflation in CEE Countries?," Prague Economic Papers, Prague University of Economics and Business, vol. 2018(2), pages 131-148.
    31. Sebastian Dullien & Ulrich Fritsche, 2007. "Does the Dispersion of Unit Labor Cost Dynamics in the EMU Imply Long-run Divergence? Results from a Comparison with the United States of America and Germany," Macroeconomics and Finance Series 200702, University of Hamburg, Department of Socioeconomics.
    32. Andrzej Toroj, 2009. "Macroeconomic adjustment and heterogeneity in the euro area," NBP Working Papers 54, Narodowy Bank Polski.
    33. Ailenei, Dorel & Cristescu, Amalia, 2010. "Regional Distribution of Inflationary Pressures in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 32-43, December.
    34. Giulio PALOMBA & Emma SARNO & Alberto ZAZZARO, 2007. "Testing similarities of short-run inflation dynamics among EU countries after the Euro," Working Papers 289, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    35. Uwe Blien & Hermann Gartner & Heiko Stüber & Katja Wolf, 2009. "Regional price levels and the agglomeration wage differential in western Germany," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(1), pages 71-88, March.
    36. Yilmazkuday, Hakan, 2009. "Inflation Targeting and Inflation Convergence within Turkey," MPRA Paper 16770, University Library of Munich, Germany.
    37. Jun Nagayasu, 2017. "Regional inflation, spatial locations and the Balassa-Samuelson effect: Evidence from Japan," Urban Studies, Urban Studies Journal Limited, vol. 54(6), pages 1482-1499, May.
    38. Martin Brown & Ralph De Haas & Vladimir Sokolov, 2013. "Regional inflation and financial dollarisation," Working Papers 163, European Bank for Reconstruction and Development, Office of the Chief Economist.

  104. Marcellino, Massimiliano & Kapetanios, George, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers.

    Cited by:

    1. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    2. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    3. Dominique Guégan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Economics Bulletin, AccessEcon, vol. 30(1), pages 508-518.
    4. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    5. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate Pass-Through in the Major European Economies - The Role of Expectations," Discussion Papers 10/03, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    6. Alain Kabundi & Francisco Nadal De Simone, 2011. "France in the global economy: a structural approximate dynamic factor model analysis," Empirical Economics, Springer, vol. 41(2), pages 311-342, October.
    7. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    8. Hofmann, Boris, 2009. "Do monetary indicators lead euro area inflation?," Journal of International Money and Finance, Elsevier, vol. 28(7), pages 1165-1181, November.
    9. Falk Brauning & Siem Jan Koopman, 2012. "Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis," Tinbergen Institute Discussion Papers 12-042/4, Tinbergen Institute.
    10. Forni, Mario & Di Bonaventura, Luca & Pattarin, Francesco, 2018. "The Forcasting Performance of Dynamic Factor Models with Vintage Data," CEPR Discussion Papers 13034, C.E.P.R. Discussion Papers.
    11. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    12. Boriss Siliverstovs & Konstantin A. Kholodilin, 2006. "On Selection of Components for a Diffusion Index Model: It's not the Size, It's How You Use It," Discussion Papers of DIW Berlin 598, DIW Berlin, German Institute for Economic Research.
    13. Forni, Mario & Giovannelli, Alessandro & Lippi, Marco & Soccorsi, Stefano, 2016. "Dynamic Factor model with infinite dimensional factor space: forecasting," CEPR Discussion Papers 11161, C.E.P.R. Discussion Papers.
    14. Camba-Méndez, Gonzalo & Kapetanios, George, 2004. "Forecasting euro area inflation using dynamic factor measures of underlying inflation," Working Paper Series 402, European Central Bank.
    15. B. Jungbacker & S.J. Koopman & M. van Der Wel, 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Post-Print hal-00828980, HAL.
    16. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Post-Print halshs-00460461, HAL.
    17. Sandra Eickmeier & Joerg Breitung, 2006. "Business cycle transmission from the euro area to CEECs," Computing in Economics and Finance 2006 229, Society for Computational Economics.
    18. Ali Babikir & Henry Mwambi, 2016. "Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods," Empirical Economics, Springer, vol. 51(4), pages 1541-1556, December.
    19. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    20. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    21. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    22. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    23. Heather Anderson & Fashid Vahid, 2005. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," ANU Working Papers in Economics and Econometrics 2005-451, Australian National University, College of Business and Economics, School of Economics.
    24. Choi, In, 2012. "Efficient Estimation Of Factor Models," Econometric Theory, Cambridge University Press, vol. 28(2), pages 274-308, April.
    25. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank.
    26. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Post-Print halshs-00460472, HAL.
    27. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," PSE-Ecole d'économie de Paris (Postprint) halshs-00460461, HAL.
    28. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    29. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    30. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    31. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," PSE-Ecole d'économie de Paris (Postprint) halshs-00511979, HAL.
    32. Dominique Guegan & Patrick Rakotomarolahy, 2009. "The Multivariate k-Nearest Neighbor Model for Dependent Variables : One-Sided Estimation and Forecasting," Post-Print halshs-00423871, HAL.
    33. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," Borradores de Economia 643, Banco de la Republica de Colombia.
    34. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    35. George Kapetanios & Massimiliano Marcellino, 2006. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," Working Papers 577, Queen Mary University of London, School of Economics and Finance.
    36. In Choi & Jorg Breitung, 2011. "Factor models," Working Papers 1121, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Dec 2011.
    37. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    38. Shinya Tanaka & Eiji Kurozumi, 2010. "Investigating Finite Sample Properties of Estimators for Approximate Factor Models When N Is Small," Global COE Hi-Stat Discussion Paper Series gd10-156, Institute of Economic Research, Hitotsubashi University.
    39. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2013. "How Do Anticipated Changes to Short-Term Market Rates Influence Banks' Retail Interest Rates? Evidence from the Four Major Euro Area Economies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1375-1414, October.
    40. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    41. Oliver Hülsewig & Johannes Mayr & Timo Wollmershäuser, 2008. "Forecasting Euro Area Real GDP: Optimal Pooling of Information," CESifo Working Paper Series 2371, CESifo.
    42. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    43. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2009. "GDP nowcasting with ragged-edge data : A semi-parametric modelling," Post-Print halshs-00344839, HAL.
    44. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
    45. Dimitar EFTIMOSKI, 2019. "Improving Short-Term Forecasting of Macedonian GDP: Comparing the Factor Model with the Macroeconomic Structural Equation Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 32-53, June.
    46. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    47. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00511979, HAL.
    48. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," PSE-Ecole d'économie de Paris (Postprint) halshs-00460472, HAL.
    49. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    50. Tjeerd M. Boonman & Jan P.A.M. Jacobs & Gerard H. Kuper, 2011. "Why didn't the Global Financial Crisis hit Latin America?," CIRANO Working Papers 2011s-63, CIRANO.
    51. Tjeerd M. Boonman & Andrea E. Sanchez Urbina, 2020. "Extreme Bounds Analysis in Early Warning Systems for Currency Crises," Open Economies Review, Springer, vol. 31(2), pages 431-470, April.
    52. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A short note on the nowcasting and the forecasting of Euro-area GDP using non-parametric techniques," Post-Print halshs-00461711, HAL.
    53. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.
    54. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00505165, HAL.
    55. Gupta, Rangan & Kabundi, Alain, 2011. "A large factor model for forecasting macroeconomic variables in South Africa," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1076-1088, October.
    56. B. Jungbacker & S.J. Koopman & M. van der Wel, 2009. "Dynamic Factor Analysis in The Presence of Missing Data," Tinbergen Institute Discussion Papers 09-010/4, Tinbergen Institute, revised 11 Mar 2011.

  105. Brüggemann, Ralf & Lütkepohl, Helmut & Marcellino, Massimiliano, 2006. "Forecasting euro-area variables with German pre-EMU data," SFB 649 Discussion Papers 2006-065, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Stelios D. Bekiros & Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2016. "Dealing with Financial Instability under a DSGE modeling approach with Banking Intermediation: a predictability analysis versus TVP-VARs," Open Access publications 10197/7323, School of Economics, University College Dublin.
    2. Heather M. Anderson & Mardi Dungey & Denise R Osborn & Farshid Vahid, 2010. "Financial Integration and the Construction of Historical Financial Data for the Euro Area," Centre for Growth and Business Cycle Research Discussion Paper Series 152, Economics, The University of Manchester.
    3. Michael Ehrmann & Marcel Fratzscher & Roberto Rigobon, 2011. "Stocks, bonds, money markets and exchange rates: measuring international financial transmission," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 948-974, September.
    4. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Open Access publications 10197/7588, School of Economics, University College Dublin.
    5. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Amisano, Gianni & Fagan, Gabriel, 2013. "Money growth and inflation: A regime switching approach," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 118-145.
    7. Bekiros, Stelios D. & Paccagnini, Alessia, 2015. "Macroprudential Policy And Forecasting Using Hybrid Dsge Models With Financial Frictions And State Space Markov-Switching Tvp-Vars," Macroeconomic Dynamics, Cambridge University Press, vol. 19(7), pages 1565-1592, October.
    8. Stelios Bekiros & Alessia Paccagnini, 2013. "Policy-oriented macroeconomic forecasting with hybrid DGSE and time-varying parameter VAR models," Working Papers 236, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
    9. Stelios D. Bekiros & Alessia Paccagnini, 2013. "Bayesian Forecasting with a Factor-Augmented Vector Autoregressive DSGE model," Working Paper series 22_13, Rimini Centre for Economic Analysis.
    10. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    11. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
    12. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Open Access publications 10197/7322, School of Economics, University College Dublin.
    13. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    14. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
    15. Gianni Amisano & Roberta Colavecchio, 2013. "Money Growth and Inflation: evidence from a Markov Switching Bayesian VAR," Macroeconomics and Finance Series 201304, University of Hamburg, Department of Socioeconomics.
    16. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.
    17. Stelios Bekiros & Alessia Paccagnini, 2014. "Forecasting the US Economy with a Factor-Augmented Vector Autoregressive DSGE model," Working Papers 2014-183, Department of Research, Ipag Business School.

  106. Massimiliano Marcellino & George Kapetanios, 2006. "The Role of Search Frictions and Bargaining for Inflation Dynamics," Working Papers 305, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Abo-Zaid, Salem, 2013. "Optimal monetary policy and downward nominal wage rigidity in frictional labor markets," Journal of Economic Dynamics and Control, Elsevier, vol. 37(1), pages 345-364.
    2. Holt Richard, 2008. "Job Reallocation, Unemployment and Hours in a New Keynesian Model," The B.E. Journal of Macroeconomics, De Gruyter, vol. 8(1), pages 1-47, August.
    3. Kai Christoffel & James Costain & Gregory de Walque & Keith Kuester & Tobias Linzert & Stephen Millard & Olivier Pierrard, 2009. "Inflation dynamics with labour market matching : assessing alternative specifications," Working Paper Research 164, National Bank of Belgium.
    4. Burkhard Heer & Alfred Maussner, 2007. "Inflation and Output Dynamics in a Model with Labor Market Search and Capital Accumulation," CESifo Working Paper Series 2036, CESifo.

  107. Ralf Brueggemann & Helmut Luetkepohl & Massimiliano Marcellino, 2006. "Forecasting Euro-Area Variables with German Pre-EMU Data," Economics Working Papers ECO2006/30, European University Institute.

    Cited by:

    1. Stelios D. Bekiros & Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2016. "Dealing with Financial Instability under a DSGE modeling approach with Banking Intermediation: a predictability analysis versus TVP-VARs," Open Access publications 10197/7323, School of Economics, University College Dublin.
    2. Heather M. Anderson & Mardi Dungey & Denise R Osborn & Farshid Vahid, 2010. "Financial Integration and the Construction of Historical Financial Data for the Euro Area," Centre for Growth and Business Cycle Research Discussion Paper Series 152, Economics, The University of Manchester.
    3. Michael Ehrmann & Marcel Fratzscher & Roberto Rigobon, 2011. "Stocks, bonds, money markets and exchange rates: measuring international financial transmission," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 948-974, September.
    4. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Open Access publications 10197/7588, School of Economics, University College Dublin.
    5. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Amisano, Gianni & Fagan, Gabriel, 2013. "Money growth and inflation: A regime switching approach," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 118-145.
    7. Bekiros, Stelios D. & Paccagnini, Alessia, 2015. "Macroprudential Policy And Forecasting Using Hybrid Dsge Models With Financial Frictions And State Space Markov-Switching Tvp-Vars," Macroeconomic Dynamics, Cambridge University Press, vol. 19(7), pages 1565-1592, October.
    8. Stelios Bekiros & Alessia Paccagnini, 2013. "Policy-oriented macroeconomic forecasting with hybrid DGSE and time-varying parameter VAR models," Working Papers 236, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
    9. Stelios D. Bekiros & Alessia Paccagnini, 2013. "Bayesian Forecasting with a Factor-Augmented Vector Autoregressive DSGE model," Working Paper series 22_13, Rimini Centre for Economic Analysis.
    10. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    11. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
    12. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Open Access publications 10197/7322, School of Economics, University College Dublin.
    13. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    14. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
    15. Gianni Amisano & Roberta Colavecchio, 2013. "Money Growth and Inflation: evidence from a Markov Switching Bayesian VAR," Macroeconomics and Finance Series 201304, University of Hamburg, Department of Socioeconomics.
    16. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.
    17. Stelios Bekiros & Alessia Paccagnini, 2014. "Forecasting the US Economy with a Factor-Augmented Vector Autoregressive DSGE model," Working Papers 2014-183, Department of Research, Ipag Business School.

  108. Marcellino, Massimiliano, 2006. "A Simple Benchmark for Forecasts of Growth and Inflation," CEPR Discussion Papers 6012, C.E.P.R. Discussion Papers.

    Cited by:

    1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    2. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
    3. Angelini, Elena & Marcellino, Massimiliano, 2007. "Econometric analyses with backdated data: unified Germany and the euro area," Working Paper Series 752, European Central Bank.

  109. Marcellino, Massimiliano & Kapetanios, George, 2006. "Impulse Response Functions from Structural Dynamic Factor Models: A Monte Carlo Evaluation," CEPR Discussion Papers 5621, C.E.P.R. Discussion Papers.

    Cited by:

    1. George Kapetanios & Massimiliano Marcellino, 2006. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," Working Papers 577, Queen Mary University of London, School of Economics and Finance.
    2. Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007.
    3. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.

  110. Guenter Beck & Massimiliano Marcellino, 2006. "Regional Inflation Dynamics within and across Euro Area and a Comparison with the US," Computing in Economics and Finance 2006 338, Society for Computational Economics.

    Cited by:

    1. Nagayasu, Jun, 2014. "Regional inflation, spatial location and the Balassa-Samuelson effect," MPRA Paper 59220, University Library of Munich, Germany.
    2. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    3. Ulrich Fritsche & Vladimir Kuzin, 2007. "Unit Labor Cost Growth Differentials in the Euro Area, Germany, and the US: Lessons from PANIC and Cluster Analysis," Discussion Papers of DIW Berlin 667, DIW Berlin, German Institute for Economic Research.
    4. Zsuzsanna Zsibók & Balázs Varga, 2012. "Inflation Persistence in Hungary: a Spatial Analysis," Working Papers 1203, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    5. Andrea Vaona & Guido Ascari, 2012. "Regional Inflation Persistence: Evidence from Italy," Regional Studies, Taylor & Francis Journals, vol. 46(4), pages 509-523, June.
    6. Riccardo Cristadoro & Giuseppe Saporito & Fabrizio Venditti, 2013. "Forecasting inflation and tracking monetary policy in the euro area: does national information help?," Empirical Economics, Springer, vol. 44(3), pages 1065-1086, June.
    7. Giulio Palomba & Emma Sarno & Alberto Zazzaro, 2009. "Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro," Empirical Economics, Springer, vol. 37(2), pages 231-270, October.
    8. Diego Winkelried & José Enrique Gutierrez, 2015. "Regional Inflation Dynamics and Inflation Targeting. The Case of Peru," Journal of Applied Economics, Taylor & Francis Journals, vol. 18(2), pages 199-224, November.
    9. Sebastian Dullien & Ulrich Fritsche, 2008. "Does the dispersion of unit labor cost dynamics in the EMU imply long-run divergence?," International Economics and Economic Policy, Springer, vol. 5(3), pages 269-295, November.
    10. Kai Carstensen & Jan Hagen & Oliver Hossfeld & Abelardo Salazar Neaves, 2008. "Money Demand Stability and Inflation Prediction in the Four Largest EMU Countries," ifo Working Paper Series 61, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    11. Eric Girardin & Cheikh A. T. Sall, 2018. "Inflation Dynamics of Franc-Zone Countries Determinants, Co-movements and Spatial Interactions," Open Economies Review, Springer, vol. 29(2), pages 295-320, April.
    12. Serhan Cevik, 2023. "Mind the gap: city-level inflation synchronization," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 70(1), pages 121-139, March.
    13. Carla Massidda & Paolo Mattana, 2008. "Regional productivity and relative prices dynamics: the case of Italy," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(4), pages 945-966, December.
    14. Angelos Liontakis & Christos T. Papadas, 2010. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," Working Papers 2010-6, Agricultural University of Athens, Department Of Agricultural Economics.
    15. Liontakis, Angelos E. & Papadas, Christos T., 2009. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," 113th Seminar, September 3-6, 2009, Chania, Crete, Greece 58084, European Association of Agricultural Economists.
    16. Stefano IACUS & Giuseppe PORRO, 2013. "Does European Monetary Union make inflation dynamics more uniform?," Departmental Working Papers 2013-12, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    17. Aleksandra Halka & Grzegorz Szafranski, 2018. "What Common Factors are Driving Inflation in CEE Countries?," Prague Economic Papers, Prague University of Economics and Business, vol. 2018(2), pages 131-148.
    18. Sebastian Dullien & Ulrich Fritsche, 2007. "Does the Dispersion of Unit Labor Cost Dynamics in the EMU Imply Long-run Divergence? Results from a Comparison with the United States of America and Germany," Macroeconomics and Finance Series 200702, University of Hamburg, Department of Socioeconomics.
    19. Andrzej Toroj, 2009. "Macroeconomic adjustment and heterogeneity in the euro area," NBP Working Papers 54, Narodowy Bank Polski.
    20. Ailenei, Dorel & Cristescu, Amalia, 2010. "Regional Distribution of Inflationary Pressures in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 32-43, December.
    21. Uwe Blien & Hermann Gartner & Heiko Stüber & Katja Wolf, 2009. "Regional price levels and the agglomeration wage differential in western Germany," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(1), pages 71-88, March.
    22. Jun Nagayasu, 2017. "Regional inflation, spatial locations and the Balassa-Samuelson effect: Evidence from Japan," Urban Studies, Urban Studies Journal Limited, vol. 54(6), pages 1482-1499, May.

  111. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.

    Cited by:

    1. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
    2. Matthias Mohr, 2005. "A Trend-Cycle(-Season) Filter," Econometrics 0508004, University Library of Munich, Germany.
    3. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    4. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    5. Christian Dreger & Konstantin A. Kholodilin, 2006. "Prognosen der regionalen Konjunkturentwicklung," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 73(34), pages 469-474.
    6. Sandra Eickmeier & Joerg Breitung, 2006. "Business cycle transmission from the euro area to CEECs," Computing in Economics and Finance 2006 229, Society for Computational Economics.
    7. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    8. Lorenzo Boldrini & Eric Hillebrand, 2015. "Supervision in Factor Models Using a Large Number of Predictors," CREATES Research Papers 2015-38, Department of Economics and Business Economics, Aarhus University.
    9. Eickmeier, Sandra & Breitung, Jorg, 2006. "How synchronized are new EU member states with the euro area? Evidence from a structural factor model," Journal of Comparative Economics, Elsevier, vol. 34(3), pages 538-563, September.
    10. Ennio Cascetta & Francesca Pagliara & Andrea Papola, 2007. "Alternative approaches to trip distribution modelling: A retrospective review and suggestions for combining different approaches," Papers in Regional Science, Wiley Blackwell, vol. 86(4), pages 597-620, November.
    11. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
    12. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    13. Christian Gillitzer & Jonathan Kearns, 2007. "Forecasting with Factors: The Accuracy of Timeliness," RBA Research Discussion Papers rdp2007-03, Reserve Bank of Australia.

  112. Favero, Carlo A. & Marcellino, Massimiliano, 2005. "Modelling and Forecasting Fiscal Variables for the euro Area," CEPR Discussion Papers 5294, C.E.P.R. Discussion Papers.

    Cited by:

    1. Philippe Burger & Estian Calitz, 2019. "Sustainable fiscal policy and economic growth in South Africa," Working Papers 15/2019, Stellenbosch University, Department of Economics.
    2. Leal, Teresa & Pérez, Javier J. & Tujula, Mika & Vidal, Jean-Pierre, 2007. "Fiscal forecasting: lessons from the literature and challenges," Working Paper Series 843, European Central Bank.
    3. Piotr Ciżkowicz & Andrzej Rzońca & Rafał Trzeciakowski, 2015. "Membership in the Euro area and fiscal sustainability - Analysis through panel fiscal reaction functions," a/ Working Papers Series 1501, Italian Association for the Study of Economic Asymmetries, Rome (Italy).
    4. Matvey Finagin & Sergey Sheremeta, 2024. "Forecasting Non-Oil and Gas Revenues in the Russian Budget," Russian Journal of Money and Finance, Bank of Russia, vol. 83(4), pages 76-97, December.
    5. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
    6. Paolo Casadio & Antonio Paradiso & B. Bhaskara Rao, 2012. "The dynamics of Italian public debt: alternative paths for fiscal consolidation," Applied Economics Letters, Taylor & Francis Journals, vol. 19(7), pages 635-639, May.
    7. Thomas A. Alexopoulos & Henry Thompson, 2021. "A macroeconomic simulation for Greece in the wake of its government debt crisis," Economic Change and Restructuring, Springer, vol. 54(3), pages 699-716, August.
    8. Adam Pigoń & Michał Ramsza, 2022. "A Comparison of German, Swiss, and Polish Fiscal Rules Using Monte Carlo Simulations," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 17-41.
    9. Carabotta, Laura & Paluzie, Elisenda & Ramos, Raul, 2017. "Does fiscal responsibility matter? Evidence from public and private forecasters in Italy," International Journal of Forecasting, Elsevier, vol. 33(3), pages 694-706.
    10. Laura Carabotta & Peter Claeys, 2015. "Combine to compete: improving fiscal forecast accuracy over time," UB School of Economics Working Papers 2015/320, University of Barcelona School of Economics.
    11. Kumar, Saten & Paradiso, Antonio, 2011. "Assessing Sustainability of the Irish Public Debt," MPRA Paper 35295, University Library of Munich, Germany.
    12. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    13. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
    14. Artem Vdovychenko, 2017. "Fiscal Policy Reaction Function and Sustainability of Fiscal Policy in Ukraine," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 240, pages 22-35.
    15. Hasko, Harri, 2007. "Some unpleasant fiscal arithmetic: the role of monetary and fiscal policy in public debt dynamics since the 1970s," Bank of Finland Research Discussion Papers 28/2007, Bank of Finland.
    16. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    17. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29.
    18. Marcellino, Massimiliano, 2006. "A Simple Benchmark for Forecasts of Growth and Inflation," CEPR Discussion Papers 6012, C.E.P.R. Discussion Papers.
    19. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    20. Thomas Flavin & Ekaterini Panopoulou & Theologos Pantelidis, 2009. "Forecasting growth and inflation in an enlarged euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 405-425.
    21. Vdovychenko Artem, 2016. "Fiscal Policy Rection and Sustainability of Fiscal Policy in Ukraine," EERC Working Paper Series 16/07e, EERC Research Network, Russia and CIS.
    22. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    23. Fildes, Robert & Wei, Yingqi & Ismail, Suzilah, 2011. "Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures," International Journal of Forecasting, Elsevier, vol. 27(3), pages 902-922, July.
    24. Nicholas Apergis & Arusha Cooray, 2013. "Forecasting fiscal variables: Only a strong growth plan can sustain the Greek austerity programs - Evidence from simultaneous and structural models," CAMA Working Papers 2013-25, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    25. Lees, Kirdan & Matheson, Troy, 2007. "Mind your ps and qs! Improving ARMA forecasts with RBC priors," Economics Letters, Elsevier, vol. 96(2), pages 275-281, August.
    26. Esposito, Piero & Paradiso, Antonio & Rao, B. Bhaskara, 2011. "The dynamics of French public debt: Paths for fiscal consolidations," MPRA Paper 32564, University Library of Munich, Germany.
    27. Checherita-Westphal, Cristina & Žďárek, Václav, 2017. "Fiscal reaction function and fiscal fatigue: evidence for the euro area," Working Paper Series 2036, European Central Bank.
    28. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    29. Steffen Henzel & Johannes Mayr, 2009. "The Virtues of VAR Forecast Pooling – A DSGE Model Based Monte Carlo Study," ifo Working Paper Series 65, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    30. R. Golinelli & I. Mammi & A. Musolesi, 2018. "Parameter heterogeneity, persistence and cross-sectional dependence: new insights on fiscal policy reaction functions for the Euro area," Working Papers wp1120, Dipartimento Scienze Economiche, Universita' di Bologna.
    31. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
    32. Philippe Burger & Krige Siebrits & Estian Calitz, 2016. "Fiscal Consolidation and the Public Sector Balance Sheet in South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 84(4), pages 501-519, December.
    33. Christoph Peatz, 2020. "Fiscal Rules in Good Times and Bad," IMK Working Paper 206-2020, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    34. Philippe Burger & Krige Siebrits & Estian Calitz, 2015. "The public sector balance sheet and fiscal consolidation in South Africa," Working Papers 11/2015, Stellenbosch University, Department of Economics.
    35. Sabaj, Ernil & Kahveci, Mustafa, 2018. "Forecasting tax revenues in an emerging economy: The case of Albania," MPRA Paper 84404, University Library of Munich, Germany.
    36. Esposito, Piero & Paradiso, Antonio & Rao, B. Bhaskara, 2011. "The dynamics of Spanish public debt and sustainable paths for fiscal consolidation," MPRA Paper 32563, University Library of Munich, Germany.

  113. Marcellino, Massimiliano, 2005. "Leading Indicators: What Have We Learned?," CEPR Discussion Papers 4977, C.E.P.R. Discussion Papers.

    Cited by:

    1. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    2. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    3. Curran, Declan & Funke, Michael, 2006. "Taking the temperature: forecasting GDP growth for mainland in China," BOFIT Discussion Papers 6/2006, Bank of Finland Institute for Emerging Economies (BOFIT).
    4. Mohsin S. Khan & Axel Schimmelpfennig, 2006. "Inflation in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 45(2), pages 185-202.
    5. John G Powell & Sirimon Treepongkaruna, 2012. "Recession fears as self-fulfilling prophecies? Influence on stock returns and output," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 231-260, August.
    6. Timmermann, Allan & Aiolfi, Marco & Catão, Luís, 2010. "Common Factors in Latin America?s Business Cycles," CEPR Discussion Papers 7671, C.E.P.R. Discussion Papers.
    7. Maria Antoinette Silgoner, 2005. "An Overview of European Economic Indicators: Great Variety of Data on the Euro Area, Need for More Extensive Coverage of the New EU Member States," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 66-89.
    8. Matta Samer, 2015. "New Coincident and Leading Indexes for the Lebanese Economy," Review of Middle East Economics and Finance, De Gruyter, vol. 11(3), pages 277-303, December.
    9. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    10. Croce, Roberto M. & Haurin, Donald R., 2009. "Predicting turning points in the housing market," Journal of Housing Economics, Elsevier, vol. 18(4), pages 281-293, December.
    11. De Bandt. O. & Bruneau, C. & Flageollet, B., 2006. "Assessing Aggregate Comovements in France, Germany and Italy. Using a Non Stationary Factor Model of the Euro Area," Working papers 145, Banque de France.
    12. Vladimir Dubrovskiy & Inna Golodniuk & Janusz Szyrmer, 2009. "Composite Leading Indicators for Ukraine: An Early Warning Model," CASE Network Reports 0085, CASE-Center for Social and Economic Research.
    13. Idrovo Aguirre, Byron, 2007. "Los Ciclos del Mercado Inmobiliario y su Relación con los Ciclos de la Economía [Housing Market Fluctuations and the Economic Cycles]," MPRA Paper 19365, University Library of Munich, Germany, revised 24 Sep 2007.
    14. Muriel Nguiffo-Boyom, 2008. "A monthly indicator of Economic activity for Luxembourg," BCL working papers 31, Central Bank of Luxembourg.

  114. Farmer, Roger & Henry, Jerome & Marcellino, Massimiliano & Beyer, Andreas, 2005. "Factor Analysis in a New-Keynesian Model," CEPR Discussion Papers 5266, C.E.P.R. Discussion Papers.

    Cited by:

    1. Jian Gao & Gang Gong & Xue-Zhong He, 2007. "Monetary Policy and Exchange Rate Regime: Proposal for a Small and Less Developed Economy," Research Paper Series 199, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Renée B Adams & Roman Kräussl & Marco Navone & Patrick Verwijmeren & Stijn Van Nieuwerburgh, 2021. "Gendered Prices [Can culture affect prices? A cross-cultural study of shopping and retail prices]," The Review of Financial Studies, Society for Financial Studies, vol. 34(8), pages 3789-3839.
      • Renée B Adams & Roman Kräussl & Marco Navone & Patrick Verwijmeren, 2021. "Gendered Prices," Published Paper Series 2021-4, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    3. George Kapetanios & Massimiliano Marcellino, 2008. "Cross-sectional Averaging and Instrumental Variable Estimation with Many Weak Instruments," Working Papers 627, Queen Mary University of London, School of Economics and Finance.
    4. George Kapetanios & Massimiliano Marcellino, 2006. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," Working Papers 577, Queen Mary University of London, School of Economics and Finance.
    5. David Parsley & Helen Popper, 2009. "Evaluating Exchange Rate Management An Application to Korea," Working Papers 282009, Hong Kong Institute for Monetary Research.
    6. Andrea Carriero, 2007. "A Simple Test of the New Keynesian Phillips Curve," Working Papers 592, Queen Mary University of London, School of Economics and Finance.
    7. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2007. "Factor Analysis in a Model with Rational Expectations," NBER Working Papers 13404, National Bureau of Economic Research, Inc.
    8. Andreas Beyer & Roger E.A. Farmer, 2005. "Measuring the Effects of Real and Monetary Shocks in a Structural New-Keynesian Model," Computing in Economics and Finance 2005 172, Society for Computational Economics.
    9. Beyer, Andreas & Farmer, Roger E. A., 2006. "A method to generate structural impulse-responses for measuring the effects of shocks in structural macro models," Working Paper Series 586, European Central Bank.

  115. Marcellino, Massimiliano, 2005. "Pooling-based data interpolation and backdating," CEPR Discussion Papers 5295, C.E.P.R. Discussion Papers.

    Cited by:

    1. Schumacher, Christian & Marcellino, Massimiliano & Foroni, Claudia, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.
    2. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    3. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    4. Mateusz Pipień & Sylwia Roszkowska, 2015. "Szacunki kwartalnego PKB w polskich województwach," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 5, pages 145-169.
    5. Mateusz Pipień & Sylwia Roszkowska, 2015. "Quarterly estimates of regional GDP in Poland – application of statistical inference of functions of parameters," NBP Working Papers 219, Narodowy Bank Polski.

  116. Stock, James & Watson, Mark & Marcellino, Massimiliano, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers.

    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 15-084/III, Tinbergen Institute, revised 03 Jul 2017.
    2. Christian Hutter & Enzo Weber, 2015. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3540-3558, July.
    3. Mario Porqueddu & Fabrizio Venditti, 2012. "Do food commodity prices have asymmetric effects on Euro-Area inflation?," Temi di discussione (Economic working papers) 878, Bank of Italy, Economic Research and International Relations Area.
    4. Guillaume Chevillon, 2006. "Multi-step Forecasting in Unstable Economies: Robustness Issues in the Presence of Location Shifts," Economics Series Working Papers 257, University of Oxford, Department of Economics.
    5. Buncic, Daniel & Müller, Oliver, 2017. "Measuring the output gap in Switzerland with linear opinion pools," Economic Modelling, Elsevier, vol. 64(C), pages 153-171.
    6. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    7. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    8. Guilherme Schultz Lindenmeyer & Hudson Silva Torrent, 2024. "Boosting and Predictability of Macroeconomic Variables: Evidence from Brazil," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 377-409, July.
    9. Jane M. Binner & C. Thomas Elger & Barry E. Jones & Birger Nilsson, 2006. "Forecasting Inflation: the Relevance of Higher Moments," Computing in Economics and Finance 2006 407, Society for Computational Economics.
    10. Leonardo Nogueira Ferreira & Silvia Miranda-Agrippino & Giovanni Ricco, 2023. "Bayesian Local Projections," Working Papers Series 581, Central Bank of Brazil, Research Department.
    11. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    12. Gachoki Emilio Munene, 2023. "Foreign Direct Investment, Trade Openness and Economic Growth in Kenya: Empirical Analysis Using ARDL Approach," International Journal of Science and Business, IJSAB International, vol. 28(1), pages 115-126.
    13. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    14. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-time inflation forecasting in a changing world," Staff Reports 388, Federal Reserve Bank of New York.
    15. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    16. Oriol Gonzalez-Casasus & Frank Schorfheide, 2025. "Misspecification-Robust Shrinkage and Selection for VAR Forecasts and IRFs," PIER Working Paper Archive 25-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    17. Hwee Kwan Chow & Keen Meng Choy, 2008. "Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore," Economic Growth Centre Working Paper Series 0802, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    18. María Alejandra Hernández-Montes & Ramón Hernández-Ortega & Jonathan Alexander Muñoz-Martínez, 2022. "Aporte de las expectativas de empresarios al pronóstico de las variables macroeconómicas," Borradores de Economia 1202, Banco de la Republica de Colombia.
    19. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    20. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    21. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate Pass-Through in the Major European Economies - The Role of Expectations," Discussion Papers 10/03, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    22. Nicholas Taylor, 2014. "Economic forecast quality: information timeliness and data vintage effects," Empirical Economics, Springer, vol. 46(1), pages 145-174, February.
    23. Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
    24. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    25. Oscar Jorda & Massimiliano Marcellino, 2008. "Path Forecast Evaluation," Working Papers 131, University of California, Davis, Department of Economics.
    26. Gary Koop, 2011. "Forecasting with Medium and Large Bayesian VARs," Working Papers 1117, University of Strathclyde Business School, Department of Economics.
    27. Medel, Carlos A., 2017. "Forecasting Chilean Inflation with the Hybrid New Keynesian Phillips Curve: Globalisation, Combination, and Accuracy," MPRA Paper 78439, University Library of Munich, Germany.
    28. Yuri S. Popkov & Alexey Yu. Popkov & Yuri A. Dubnov & Dimitri Solomatine, 2020. "Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models," Mathematics, MDPI, vol. 8(7), pages 1-20, July.
    29. Ruch,Franz Ulrich, 2021. "Neutral Real Interest Rates in Inflation Targeting Emerging and Developing Economies," Policy Research Working Paper Series 9711, The World Bank.
    30. Schumacher, Christian & Marcellino, Massimiliano & Foroni, Claudia, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.
    31. Gaglianone, Wagner Piazza & Dutra Areosa, Waldyr, 2017. "Financial Conditions Indicator for Brazil," IDB Publications (Working Papers) 8488, Inter-American Development Bank.
    32. Marco Barassi & Yuqian Zhao, 2018. "Combination Forecasting of Energy Demand in the UK," The Energy Journal, , vol. 39(1_suppl), pages 209-238, June.
    33. Klaus Abberger & Michael Graff & Boriss Siliverstovs & Jan-Egbert Sturm, 2014. "The KOF Economic Barometer, Version 2014," KOF Working papers 14-353, KOF Swiss Economic Institute, ETH Zurich.
    34. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    35. Laura Liu, 2018. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," Papers 1805.04178, arXiv.org, revised Oct 2021.
    36. Wooi Chen Khoo & Seng Huat Ong & Biswas Atanu, 2022. "Coherent Forecasting for a Mixed Integer-Valued Time Series Model," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    37. Konstantin Kholodilin, 2014. "Business confidence and forecasting of housing prices and rents in large German cities," ERSA conference papers ersa14p9, European Regional Science Association.
    38. Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
    39. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    40. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2010. "Forecasting with Factor-augmented Error Correction Models," CEPR Discussion Papers 7677, C.E.P.R. Discussion Papers.
    41. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    42. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    43. Jed Armstrong, 2015. "The Reserve Bank of New Zealand’s output gap indicator suite and its real-time properties," Reserve Bank of New Zealand Analytical Notes series AN2015/08, Reserve Bank of New Zealand.
    44. Proietti, Tommaso, 2011. "The Multistep Beveridge-Nelson Decomposition," Working Papers 09/2011, University of Sydney Business School, Discipline of Business Analytics.
    45. Catania, Leopoldo & Luati, Alessandra, 2023. "Semiparametric modeling of multiple quantiles," Journal of Econometrics, Elsevier, vol. 237(2).
    46. Buncic, Daniel & Piras, Gion Donat, 2014. "Heterogeneous Agents, the Financial Crisis and Exchange Rate Predictability," Economics Working Paper Series 1436, University of St. Gallen, School of Economics and Political Science, revised Oct 2015.
    47. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    48. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2017. "The transmission of monetary policy shocks," Bank of England working papers 657, Bank of England.
    49. Eliana González Molano & Luis Fernando Melo Velandia & Anderson Grajales Olarte, 2007. "Pronósticos directos de la inflación colombiana," Borradores de Economia 458, Banco de la Republica de Colombia.
    50. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    51. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
    52. Duprey, Thibaut & Klaus, Benjamin, 2017. "How to predict financial stress? An assessment of Markov switching models," Working Paper Series 2057, European Central Bank.
    53. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    54. Luca Brugnolini, 2018. "About Local Projection Impulse Response Function Reliability," CEIS Research Paper 440, Tor Vergata University, CEIS, revised 09 Jun 2018.
    55. Paulo Mauricio Sánchez Beltrán & Luis Fernando Melo Velandia, 2013. "Combinación de brechas del producto colombiano," Borradores de Economia 10973, Banco de la Republica.
    56. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    57. Wagner Piazza Gaglianone & Luiz Renato Lima, 2014. "Constructing Optimal Density Forecasts From Point Forecast Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 736-757, August.
    58. Falk Brauning & Siem Jan Koopman, 2012. "Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis," Tinbergen Institute Discussion Papers 12-042/4, Tinbergen Institute.
    59. Lorenzo Fratoni & Susanna Levantesi & Massimiliano Menzietti, 2022. "Measuring Financial Sustainability and Social Adequacy of the Italian NDC Pension System under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    60. Eliana González & . Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model for the Colombian Inflation," Borradores de Economia 549, Banco de la Republica de Colombia.
    61. Rebecca Stuart, 2020. "Monetary regimes, the term structure and business cycles in Ireland, 1972–2018," Manchester School, University of Manchester, vol. 88(5), pages 731-748, September.
    62. Michiel De Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," International Finance Discussion Papers 993, Board of Governors of the Federal Reserve System (U.S.).
    63. Jalal Shiri & Shahaboddin Shamshirband & Ozgur Kisi & Sepideh Karimi & Seyyed M Bateni & Seyed Hossein Hosseini Nezhad & Arsalan Hashemi, 2016. "Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5217-5229, November.
    64. Vieira, Fausto & Fernandes, Marcelo & Chague, Fernando, 2017. "Forecasting the Brazilian yield curve using forward-looking variables," International Journal of Forecasting, Elsevier, vol. 33(1), pages 121-131.
    65. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    66. BRAIONE, Manuela, 2016. "A time-varying long run HEAVY model," LIDAM Discussion Papers CORE 2016002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    67. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    68. Stefano Giglio & Ian Dew-Becker & David Berger, 2017. "Uncertainty Shocks as Second-Moment News Shocks," 2017 Meeting Papers 403, Society for Economic Dynamics.
    69. Koop, Gary & Korobilis, Dimitris, 2012. "Large time-varying parameter VARs," MPRA Paper 38591, University Library of Munich, Germany.
    70. Helmut Herwartz, 2011. "Forecast accuracy and uncertainty in applied econometrics: a recommendation of specific-to-general predictor selection," Empirical Economics, Springer, vol. 41(2), pages 487-510, October.
    71. Turgut Kisinbay & Chikako Baba, 2011. "Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations," IMF Working Papers 2011/235, International Monetary Fund.
    72. Magdalena Grothe & Aidan Meyler, 2018. "Inflation Forecasts: Are Market-Based and Survey-Based Measures Informative?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(1), pages 171-188, January.
    73. Alfred A. Haug & Christie Smith, 2007. "Local linear impulse responses for a small open economy," Working Papers 0707, University of Otago, Department of Economics, revised Apr 2007.
    74. Valerie A. Ramey, 2016. "Macroeconomic Shocks and Their Propagation," NBER Working Papers 21978, National Bureau of Economic Research, Inc.
    75. Joel Hasbrouck, 2021. "Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 395-430.
    76. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    77. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    78. Mathias Drehmann & Mikael Juselius & Anton Korinek, 2023. "Long-term debt propagation and real reversals," BIS Working Papers 1098, Bank for International Settlements.
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    353. Daoui Marouane, 2023. "Macroeconomic Forecasting using Dynamic Factor Models: The Case of Morocco," Papers 2302.14180, arXiv.org, revised May 2023.
    354. Paolo Andreini & Donato Ceci, 2019. "A Horse Race in High Dimensional Space," CEIS Research Paper 452, Tor Vergata University, CEIS, revised 14 Feb 2019.
    355. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    356. Enders, Walter & Pascalau, Razvan, 2015. "Pretesting for multi-step-ahead exchange rate forecasts with STAR models," International Journal of Forecasting, Elsevier, vol. 31(2), pages 473-487.
    357. Leopoldo Catania & Mads Sandholdt, 2019. "Bitcoin at High Frequency," JRFM, MDPI, vol. 12(1), pages 1-20, February.
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    361. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    362. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.
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    367. Matthias Burgert & Stephane Dees, 2009. "Forecasting World Trade: Direct Versus “Bottom-Up” Approaches," Open Economies Review, Springer, vol. 20(3), pages 385-402, July.
    368. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    369. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2021. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Tinbergen Institute Discussion Papers 21-016/III, Tinbergen Institute.
    370. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Forecasting Cryptocurrencies Financial Time Series," Working Papers No 5/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    371. Zhen Zeng & Rachneet Kaur & Suchetha Siddagangappa & Saba Rahimi & Tucker Balch & Manuela Veloso, 2023. "Financial Time Series Forecasting using CNN and Transformer," Papers 2304.04912, arXiv.org.
    372. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    373. João Valle e Azevedo, 2013. "Macroeconomic Forecasting Using Low-Frequency Filters," Working Papers w201301, Banco de Portugal, Economics and Research Department.
    374. Stephen McKnight & Alexander Mihailov & Fabio Rumler, 2018. "NKPC-Based Inflation Forecasts with a Time-Varying Trend," Serie documentos de trabajo del Centro de Estudios Económicos 2018-05, El Colegio de México, Centro de Estudios Económicos.
    375. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
    376. Guillaume Chevillon, 2004. "A Comparison of Multi-step GDP Forecasts for South Africa," Documents de Travail de l'OFCE 2004-13, Observatoire Francais des Conjonctures Economiques (OFCE).
    377. Tarassow, Artur, 2019. "Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques," International Journal of Forecasting, Elsevier, vol. 35(2), pages 443-457.
    378. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    379. John Akuma & Isaac Doku & Nathaniel Awer, 2017. "Credit Risk, Deposit Mobilization and Profitability of Ghanaian Banks," International Journal of Economics and Financial Issues, Econjournals, vol. 7(5), pages 394-399.
    380. John Haywood & Granville Tunnicliffe Wilson, 2009. "A test for improved multi‐step forecasting," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 682-707, November.
    381. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    382. George Kapetanios & Fotis Papailias, 2022. "Investigating the predictive ability of ONS big data‐based indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 252-258, March.
    383. Emil Stavrev, 2010. "Measures of underlying inflation in the euro area: assessment and role for informing monetary policy," Empirical Economics, Springer, vol. 38(1), pages 217-239, February.
    384. Iania, Leonardo & Algieri, Bernardina & Leccadito, Arturo, 2022. "Forecasting total energy’s CO2 emissions," LIDAM Discussion Papers LFIN 2022003, Université catholique de Louvain, Louvain Finance (LFIN).
    385. Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan, 2019. "Forecasting Realized Volatility of Agricultural Commodity Futures with Infinite Hidden Markov HAR Models," QBS Working Paper Series 2019/10, Queen's University Belfast, Queen's Business School.
    386. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    387. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    388. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    389. Harun Özkan & M. Yazgan, 2015. "Is forecasting inflation easier under inflation targeting?," Empirical Economics, Springer, vol. 48(2), pages 609-626, March.
    390. Vermeulen, Philip, 2014. "An evaluation of business survey indices for short-term forecasting: Balance method versus Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 30(4), pages 882-897.
    391. Christos Avdoulas & Stelios Bekiros, 2018. "Nonlinear Forecasting of Euro Area Industrial Production Using Evolutionary Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 521-530, August.
    392. Ing, Ching-Kang & Sin, Chor-yiu & Yu, Shu-Hui, 2012. "Model selection for integrated autoregressive processes of infinite order," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 57-71.
    393. Aparicio, Diego & Bertolotto, Manuel I., 2020. "Forecasting inflation with online prices," International Journal of Forecasting, Elsevier, vol. 36(2), pages 232-247.
    394. George Kapetanios & Fotis Papailias, 2022. "An Evaluation Framework for Targeted Indicators Aggregates vs. Disaggregates," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-17, Economic Statistics Centre of Excellence (ESCoE).
    395. Polbin, Andrey & Shumilov, Andrei, 2023. "Прогнозирование Инфляции В России С Помощью Tvp-Модели С Байесовским Сжатием Параметров [Forecasting inflation in Russia using a TVP model with Bayesian shrinkage]," MPRA Paper 118650, University Library of Munich, Germany.
    396. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    397. Zihao Zhang & Stefan Zohren, 2021. "Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units," Papers 2105.10430, arXiv.org, revised Aug 2021.
    398. Dirk Ulbricht, 2016. "It is not structural breaks that earn average forecasts their fame," Economics Bulletin, AccessEcon, vol. 36(2), pages 1250-1259.
    399. Lanne, Markku & Nyberg, Henri & Saarinen, Erkka, 2011. "Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison," MPRA Paper 30254, University Library of Munich, Germany.
    400. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.

  117. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    2. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate Pass-Through in the Major European Economies - The Role of Expectations," Discussion Papers 10/03, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    3. Angelini, Elena & Henry, Jerome & Marcellino, Massimiliano, 2006. "Interpolation and backdating with a large information set," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2693-2724, December.
    4. Bruneau, C. & De Bandt, O. & Flageollet, A. & Michaux, E., 2003. "Forecasting Inflation using Economic Indicators: the Case of France," Working papers 101, Banque de France.
    5. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    6. Victor Bystrov, 2006. "Forecasting Emerging Market Indicators: Brazil and Russia," Economics Working Papers ECO2006/12, European University Institute.
    7. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," ifo Working Paper Series 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

  118. Artis, Michael & Marcellino, Massimiliano & Proietti, Tommaso, 2004. "Characterizing the Business Cycle for Accession Countries," CEPR Discussion Papers 4457, C.E.P.R. Discussion Papers.

    Cited by:

    1. Hanus, Lubos & Vacha, Lukas, 2015. "Business cycle synchronization of the Visegrad Four and the European Union," FinMaP-Working Papers 42, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    2. Zsolt Darvas & György Szapáry, 2008. "Business Cycle Synchronization in the Enlarged EU," Open Economies Review, Springer, vol. 19(1), pages 1-19, February.
    3. Faruk Balli & Syed Abul Basher & Hatice Ozer Balli, 2013. "International Income Risk-Sharing and the Global Financial Crisis of 2008- 2009," CAMA Working Papers 2013-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Ivan Todorov, 2012. "European Economic Integration Theories and Criteria," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 131-152.
    5. Camacho, Maximo & Perez-Quiros, Gabriel & Saiz, Lorena, 2008. "Do European business cycles look like one?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2165-2190, July.
    6. Konstantins Benkovskis, 2006. "The Effect of Latvian Pension Reform on Savings and Government Budget," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(1), pages 3-21, July.
    7. Fabrizio Carmignani, 2009. "Endogenous optimal currency areas: The case of the Central African Economic and Monetary Community," Discussion Papers Series 390, School of Economics, University of Queensland, Australia.
    8. Fidrmuc, Jarko & Korhonen, Iikka, 2006. "Meta-analysis of the business cycle correlation between the euro area and the CEECs," Journal of Comparative Economics, Elsevier, vol. 34(3), pages 518-537, September.
    9. Lubos Hanus & Lukas Vacha, 2015. "Business cycle synchronization within the European Union: A wavelet cohesion approach," Papers 1506.03106, arXiv.org, revised Feb 2016.
    10. Nektarios Aslanidis, 2010. "Business Cycle Synchronization Between The Ceec And The Euro‐Area: Evidence From Threshold Seemingly Unrelated Regressions," Manchester School, University of Manchester, vol. 78(6), pages 538-555, December.
    11. Aslanidis, Nektarios, 2007. "Business Cycle Regimes in CEECs Production: A Threshold SURE Approach," Working Papers 2072/5318, Universitat Rovira i Virgili, Department of Economics.
    12. Crespo-Cuaresma, Jesús & Fernández-Amador, Octavio, 2013. "Business cycle convergence in EMU: A second look at the second moment," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 239-259.
    13. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    14. Rátfai, Attila & Benczúr, Péter, 2005. "Economic Fluctuations in Central and Eastern Europe: The Facts," CEPR Discussion Papers 4846, C.E.P.R. Discussion Papers.
    15. Luboš Hanus & Lukáš Vácha, 2020. "Growth cycle synchronization of the Visegrad Four and the European Union," Empirical Economics, Springer, vol. 58(4), pages 1779-1795, April.
    16. Jitka Poměnková, 2010. "An Alternative Approach to the Dating of Business Cycle: Nonparametric Kernel Estimation," Prague Economic Papers, Prague University of Economics and Business, vol. 2010(3), pages 251-272.
    17. Larry Sawers, 2006. "Inequality and the Transition: Regional Development in Lithuania," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(1), pages 37-51, July.
    18. Gammadigbé, Vigninou, 2012. "Les cycles économiques des pays de l'UEMOA: synchrones ou déconnectés? [Business cycles in the WAEMU countries: synchronous or disconnected?]," MPRA Paper 39400, University Library of Munich, Germany, revised Jun 2012.
    19. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank.
    20. Veaceslav Grigoras & Irina Eusignia Stanciu, 2016. "New evidence on the (de)synchronisation of business cycles: Reshaping the European business cycle," International Economics, CEPII research center, issue 147, pages 27-52.
    21. Ageliki Anagnostou & Ioannis Panteladis & Maria Tsiapa, 2015. "Disentangling different patterns of business cycle synchronicity in the EU regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 615-641, August.
    22. Máximo Camacho & Gabriel Pérez-Quirós & Lorena Saiz, 2004. "Are european business cycles close enough to be just one?," Working Papers 0408, Banco de España.
    23. António Afonso & Davide Furceri, 2007. "Sectoral Business Cycle Synchronization in the European Union," Working Papers Department of Economics 2007/02, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    24. Sanvi Avouyi-Dovi & Rafał Kierzenkowski & Catherine Lubochinsky, 2006. "Cycles réel et du crédit : convergence ou divergence ?. Une comparaison Pologne, Hongrie, République tchèque et zone euro," Revue économique, Presses de Sciences-Po, vol. 57(4), pages 851-879.
    25. Peter Martey Addo & Monica Billio & Dominique Guegan, 2013. "Turning point chronology for the Euro-Zone: A Distance Plot Approach," Post-Print halshs-00803457, HAL.
    26. Christos S. Savva & Kyriakos C. Neanidis & Denise R. Osborn, 2007. "Business Cycle Synchronization of the Euro Area with the New and Negotiating Member Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 91, Economics, The University of Manchester.
    27. Juergen Bierbaumer-Polly, 2012. "Regional and Sectoral Business Cycles - Key Features for the Austrian economy," EcoMod2012 4074, EcoMod.
    28. Jürgen Bierbaumer & Peter Huber & Petr Rozmahel, 2015. "The Impact of EU Accession on Regional Business Cycle Synchronisation and Sector Specialisation," WIFO Working Papers 494, WIFO.
    29. Olegs Tkacevs, 2006. "The Impact of Fiscal Policy on Prices: Does the Fiscal Theory of Price Level Matter in Latvia?," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(1), pages 23-36, July.
    30. Afonso, António & Furceri, Davide, 2007. "Business cycle synchronization and insurance mechanisms in the EU," Working Paper Series 844, European Central Bank.
    31. Jesús Crespo-Cuaresma & Octavio Fernández-Amador, 2010. "Business cycle convergence in EMU: A first look at the second moment," Working Papers 2010-22, Faculty of Economics and Statistics, Universität Innsbruck.
    32. Konstantakopoulou, Ioanna & Tsionas, Efthymios G., 2014. "Half a century of empirical evidence of business cycles in OECD countries," Journal of Policy Modeling, Elsevier, vol. 36(2), pages 389-409.
    33. Bekiros Stelios & Nguyen Duc Khuong & Uddin Gazi Salah & Sjö Bo, 2015. "Business cycle (de)synchronization in the aftermath of the global financial crisis: implications for the Euro area," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 609-624, December.
    34. Avouyi-Dovi, S. & Kierzenkowski, R. & Lubochinsky, C., 2006. "Are Business and Credit Cycles Converging or Diverging? A comparison of Poland, Hungary, the Czech Republic and the Euro Area," Working papers 144, Banque de France.
    35. Baher Ahmed Elgahry, 2020. "Regional and Interregional Business Cycle Comovement in Europe, Asia, and North America," Economics Bulletin, AccessEcon, vol. 40(4), pages 3088-3103.
    36. Kierzenkowski, R. & Oung, V., 2007. "L volution des cr dits l habitat en France : une grille d analyse en termes de cycles," Working papers 172, Banque de France.
    37. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.
    38. Jarko Fidrmuc & Iikka Korhonen, 2004. "A Meta-Analysis of Business Cycle Correlations between the Euro Area, CEECs and SEECs – What Do We Know?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 76-94.
    39. Jitka Poměnková & Svatopluk Kapounek & Roman Maršálek, 2011. "Comparison of methodological approaches to identify economic activity regularities in transition economy," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(7), pages 283-292.
    40. Narayan K. Kishor & Kyriakos C. Neanidis, 2012. "What is Driving Financial Dollarization in Transition Economies? A Dynamic Factor Analysis," Centre for Growth and Business Cycle Research Discussion Paper Series 171, Economics, The University of Manchester.
    41. Pasquale Foresti & Ugo Marani & Giuseppe Piroli, 2013. "Macroeconomic Dynamics in Four Selected New Member States of the EU," EERI Research Paper Series EERI RP 2013/14, Economics and Econometrics Research Institute (EERI), Brussels.
    42. Petr Rozmahel, 2011. "Measuring the business cycles similarity and convergence trends in the Central and Eastern European countries towards the Eurozone with respect to some unclear methodological aspects," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(2), pages 237-250.
    43. Mr. Ashoka Mody & Ms. Franziska L Ohnsorge, 2007. "Can Domestic Policies Influence Inflation?," IMF Working Papers 2007/257, International Monetary Fund.
    44. Jamel Gatfaoui & Eric Girardin, 2015. "Comovement of Chinese provincial business cycles," Post-Print hal-01456105, HAL.
    45. Jürgen Bierbaumer & Werner Hölzl, 2015. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
    46. Nikola Najman & Petr Rozmahel, 2013. "Business cycle coherence and OCA endogeneity testing during the integration period in the European Union," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(4), pages 1033-1040.
    47. Jurgita Jurgutyte, 2006. "Lithuania's Track to the Euro and the Endogeneity Hypothesis," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(1), pages 53-69, July.
    48. Gabriele Tondl & Iulia Traistaru-Siedschlag, 2006. "Regional growth cycle synchronisation with the Euro Area," Papers WP173, Economic and Social Research Institute (ESRI).
    49. Iulia Siedschlag & Gabriele Tondl, 2011. "Regional output growth synchronisation with the Euro Area," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 38(2), pages 203-221, May.
    50. Maximo Camacho & Gabriel Perez-Quiros & Lorena Saiz & Universidad de Murcia, 2006. "Do european business cycles look like one $\_?$," Computing in Economics and Finance 2006 175, Society for Computational Economics.
    51. Petr Rozmahel & Ladislava Issever Grochová & Marek Litzman, 2014. "The Effect of Asymmetries in Fiscal Policy Conducts on Business Cycle Correlation in the EU. WWWforEurope Working Paper No. 62," WIFO Studies, WIFO, number 47249.
    52. Fidrmuc, Jarko & Korhonen, Iikka, 2004. "A meta-analysis of business cycle correlation between the euro area and CEECs: What do we know - and who cares?," BOFIT Discussion Papers 20/2004, Bank of Finland Institute for Emerging Economies (BOFIT).
    53. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," ifo Working Paper Series 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    54. Dinu. Marin & Marinas, Marius Corneliu & Socol Cristian & Socol, Aura Gabriela, 2012. "Clusterization, Persistence, Dependency and Volatility of Business Cycles in an Enlarged Euro Area," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-23, June.
    55. Dumitru, Ionut, 2009. "Adoptarea euro in Romania [Euro adoption in Romania]," MPRA Paper 18612, University Library of Munich, Germany.

  119. Henry, Jerome & Marcellino, Massimiliano & Angelini, Elena, 2004. "Interpolation and Backdating with A Large Information Set," CEPR Discussion Papers 4533, C.E.P.R. Discussion Papers.

    Cited by:

    1. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    2. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    3. Schumacher, Christian & Marcellino, Massimiliano & Foroni, Claudia, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.
    4. Federica Ciocchetta & Wanda Cornacchia, 2019. "Assessing financial stability risks from the real estate market in Italy: an update," Questioni di Economia e Finanza (Occasional Papers) 493, Bank of Italy, Economic Research and International Relations Area.
    5. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    6. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Jérôme Creel & Mehdi El Herradi, 2024. "Income inequality and monetary policy in the euro area," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 332-355, January.
    8. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    9. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    10. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    11. Brunhes-Lesage, V. & Darné, O., 2008. "Why calculate a business sentiment indicator for services?," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 13, pages 21-30, Autumn.
    12. Lutz Kilian & Logan T. Lewis, 2011. "Does the Fed Respond to Oil Price Shocks?," Economic Journal, Royal Economic Society, vol. 121(555), pages 1047-1072, September.
    13. Jérôme Creel & Mehdi El Herradi, 2019. "Shocking aspects of monetary policy on income inequality in the euro area," SciencePo Working papers Main hal-03403233, HAL.
    14. Darracq Pariès, Matthieu & Maurin, Laurent, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production: perspective from large panel factor models," Working Paper Series 894, European Central Bank.
    15. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    16. Angelini, Elena & Henry, Jerome & Marcellino, Massimiliano, 2006. "Interpolation and backdating with a large information set," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2693-2724, December.
    17. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    18. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank.
    19. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    20. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    21. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    22. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    23. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.
    24. Camacho, Maximo & Lopez-Buenache, German, 2023. "Factor models for large and incomplete data sets with unknown group structure," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1205-1220.
    25. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    26. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    27. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    28. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    29. Angelini, Elena & Marcellino, Massimiliano, 2007. "Econometric analyses with backdated data: unified Germany and the euro area," Working Paper Series 752, European Central Bank.
    30. Hang Zhao & Jun Zhang & Xiaohui Wang & Hongxia Yuan & Tianlu Gao & Chenxi Hu & Jing Yan, 2021. "The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
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    Cited by:

    1. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    2. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, May.
    3. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    4. Matteo Manera & Massimiliano Serati & Michele Plotegher, 2008. "Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal," Working Papers 2008.9, Fondazione Eni Enrico Mattei.
    5. Albacete, Rebeca, 2005. "Forecasting inflation in the euro area using monthly time series models and quarterly econometric models," DES - Working Papers. Statistics and Econometrics. WS ws050401, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    7. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate Pass-Through in the Major European Economies - The Role of Expectations," Discussion Papers 10/03, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    8. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
    9. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Chakravartti, Parma & Mundle, Sudipto, 2017. "An Automatic Leading Indicator Based Growth Forecast For 2016-17 and The Outlook Beyond," Working Papers 17/193, National Institute of Public Finance and Policy.
    11. Hahn, Elke & Zekaite, Zivile & de Bondt, Gabe, 2018. "ALICE: A new inflation monitoring tool," Working Paper Series 2175, European Central Bank.
    12. Christopher L. Gilbert & Duo Qin, 2007. "Representation in Econometrics: A Historical Perspective," Working Papers 583, Queen Mary University of London, School of Economics and Finance.
    13. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    14. Katja Drechsel & Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    15. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.
    16. El-Shagi, Makram, 2011. "Inflation expectations: Does the market beat econometric forecasts?," The North American Journal of Economics and Finance, Elsevier, vol. 22(3), pages 298-319.
    17. Barbara Rossi & Tatevik Sekhposyan, 2013. "Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set," Economics Working Papers 1370, Department of Economics and Business, Universitat Pompeu Fabra.
    18. Rudrani Bhattacharya & Parma Chakravartti & Sudipto Mundle, 2019. "Forecasting India’s economic growth: a time-varying parameter regression approach," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(3), pages 205-228, September.
    19. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    20. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    21. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    22. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    23. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    24. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    25. Andrade, P. & Fourel, V. & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    26. Anindya Banerjee & Bill Russell, 2006. "A markup model for forecasting inflation for the euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(7), pages 495-511.
    27. Capistran, Carlos, 2006. "On comparing multi-horizon forecasts," Economics Letters, Elsevier, vol. 93(2), pages 176-181, November.
    28. Panopoulou, Ekaterini, 2009. "Financial variables and euro area growth: A non-parametric causality analysis," Economic Modelling, Elsevier, vol. 26(6), pages 1414-1419, November.
    29. Ciccarelli, Matteo & Mojon, Benoît, 2006. "Global Inflation," Kiel Working Papers 1337, Kiel Institute for the World Economy (IfW Kiel).
    30. Poza, Carlos & Monge, Manuel, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, Elsevier, vol. 163(C), pages 163-175.
    31. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank.
    32. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    33. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    34. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
    35. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    36. Oliver Hülsewig & Johannes Mayr & Stéphane Sorbe, 2007. "Assessing the Forecast Properties of the CESifo World Economic Climate Indicator: Evidence for the Euro Area," ifo Working Paper Series 46, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    37. Peter Vlaar & Ard den Reijer, 2004. "Forecasting inflation: An art as well as a science!," Computing in Economics and Finance 2004 148, Society for Computational Economics.
    38. In Choi, 2012. "Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons," Working Papers 1209, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    39. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    40. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank.
    41. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    42. Schumacher, Christian, 2009. "Factor forecasting using international targeted predictors: the case of German GDP," Discussion Paper Series 1: Economic Studies 2009,10, Deutsche Bundesbank.
    43. Marco Rubilar-González & Gabriel Pino, 2018. "Are Euro-Area expectations about recession phases effective to anticipate consequences of economic crises?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(2), pages 141-161, June.
    44. Karen Poghosyan & Ruben Poghosyan, 2021. "On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(1), pages 52-79, June.
    45. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    46. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    47. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 27-42, March.
    48. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.
    49. de Bondt, Gabe J. & Hahn, Elke & Zekaite, Zivile, 2021. "ALICE: Composite leading indicators for euro area inflation cycles," International Journal of Forecasting, Elsevier, vol. 37(2), pages 687-707.
    50. Travaglini, Guido, 2011. "Climate change: where is the hockey stick? evidence from millennial-scale reconstructed and updated temperature time series," MPRA Paper 35565, University Library of Munich, Germany.
    51. Favero, Carlo A. & Marcellino, Massimiliano, 2005. "Modelling and Forecasting Fiscal Variables for the euro Area," CEPR Discussion Papers 5294, C.E.P.R. Discussion Papers.
    52. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    53. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    54. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    55. Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank.
    56. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    57. Kouwenberg, Roy & Zwinkels, Remco, 2014. "Forecasting the US housing market," International Journal of Forecasting, Elsevier, vol. 30(3), pages 415-425.
    58. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    59. Diron, Marie & Mojon, Benoît, 2005. "Forecasting the central bank's inflation objective is a good rule of thumb," Working Paper Series 564, European Central Bank.
    60. Vagenas, George & Vlachokyriakou, Eleni, 2012. "Olympic medals and demo-economic factors: Novel predictors, the ex-host effect, the exact role of team size, and the “population-GDP” model revisited," Sport Management Review, Elsevier, vol. 15(2), pages 211-217.
    61. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    62. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 399-413.
    63. Massimiliano Serati & Gianni Amisano, 2008. "Building composite leading indexes in a dynamic factor model framework: a new proposal," LIUC Papers in Economics 212, Cattaneo University (LIUC).
    64. Oliver Hülsewig & Johannes Mayr & Timo Wollmershäuser, 2008. "Forecasting Euro Area Real GDP: Optimal Pooling of Information," CESifo Working Paper Series 2371, CESifo.
    65. Sarah Gelper & Christophe Croux, 2010. "On the Construction of the European Economic Sentiment Indicator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 47-62, February.
    66. Fichtner, Ferdinand & Rüffer, Rasmus & Schnatz, Bernd, 2009. "Leading indicators in a globalised world," Working Paper Series 1125, European Central Bank.
    67. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    68. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2006. "Forecasting Inflation and GDP growth: Comparison of Automatic Leading Indicator (ALI) Method with Macro Econometric Structural Models (MESMs)," Working Papers 554, Queen Mary University of London, School of Economics and Finance.
    69. Giuseppe Munda, 2012. "Beyond GDP: Methodological and measurement issues in redefining “wealth”," UHE Working papers 2012_09, Universitat Autònoma de Barcelona, Departament d'Economia i Història Econòmica, Unitat d'Història Econòmica.
    70. Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting the French index of industrial production: A comparison from bridge and factor models," Economic Modelling, Elsevier, vol. 29(6), pages 2174-2182.
    71. Travaglini, Guido, 2014. "Testing the hockey-stick hypothesis by statistical analyses of a large dataset of proxy records," MPRA Paper 55835, University Library of Munich, Germany.
    72. Barbara Roffia & Andrea Zaghini, 2008. "Excess money growth and inflation dynamics," Temi di discussione (Economic working papers) 657, Bank of Italy, Economic Research and International Relations Area.
    73. Barhoumi, K. & Brunhes-Lesage, V. & Darn , O. & Ferrara, L. & Pluyaud, B. & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Working papers 222, Banque de France.
    74. Giuseppe Munda, 2015. "Beyond Gdp: An Overview Of Measurement Issues In Redefining ‘Wealth’," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 403-422, July.
    75. El-Shagi, Makram, 2009. "Inflation Expectations: Does the Market Beat Professional Forecasts?," IWH Discussion Papers 16/2009, Halle Institute for Economic Research (IWH).
    76. Panopoulou, Ekaterini, 2007. "Predictive financial models of the euro area: A new evaluation test," International Journal of Forecasting, Elsevier, vol. 23(4), pages 695-705.
    77. Jun Wen & Samia Khalid & Hamid Mahmood & Xiuyun Yang, 2022. "Economic policy uncertainty and growth nexus in Pakistan: a new evidence using NARDL model," Economic Change and Restructuring, Springer, vol. 55(3), pages 1701-1715, August.
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    81. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2007. "Automatic Leading Indicators (ALIs) versus Macro Econometric Structural Models (MESMs): Comparison of Inflation and GDP growth Forecasting," EcoMod2007 23900072, EcoMod.
    82. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    83. Gupta, Rangan & Kabundi, Alain, 2011. "A large factor model for forecasting macroeconomic variables in South Africa," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1076-1088, October.
    84. Mehdi Seraj & Pejman Bahramian & Abdulkareem Alhassan & Rasool Dehghanzadeh Shahabad, 2020. "The validity of Rodrik’s conclusion on real exchange rate and economic growth: factor priority evidence from feature selection approach," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-6, December.

  121. Artis, Michael & Marcellino, Massimiliano & Galvão, Ana Beatriz, 2003. "The Transmission Mechanism in a Changing World," CEPR Discussion Papers 4014, C.E.P.R. Discussion Papers.

    Cited by:

    1. Aslanidis, Nektarios & Christiansen, Charlotte, 2012. "Smooth transition patterns in the realized stock–bond correlation," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 454-464.
    2. Michaelides, Panayotis G. & Papageorgiou, Theofanis & Vouldis, Angelos T., 2013. "Business cycles and economic crisis in Greece (1960–2011): A long run equilibrium analysis in the Eurozone," Economic Modelling, Elsevier, vol. 31(C), pages 804-816.
    3. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    4. Goggin, Jean & Siedschlag, Iulia, 2009. "International Transmission of Business Cycles Between Ireland and its Trading Partners," Papers WP279, Economic and Social Research Institute (ESRI).
    5. Hilberg, Björn & Grill, Michael & Metiu, Norbert, 2016. "Credit constraints and the international propagation of US financial shocks," Working Paper Series 1954, European Central Bank.
    6. Jiang, Chun & Li, Xiao-Lin & Chang, Hsu-Ling & Su, Chi-Wei, 2013. "Uncovered interest parity and risk premium convergence in Central and Eastern European countries," Economic Modelling, Elsevier, vol. 33(C), pages 204-208.
    7. Marco Duenas & Giorgio Fagiolo, 2011. "Modeling the International-Trade Network: A Gravity Approach," LEM Papers Series 2011/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying volatility, financial intermediation and monetary policy," Discussion Papers 46/2016, Deutsche Bundesbank.
    9. Giorgio Fagiolo, 2009. "The International-Trade Network: Gravity Equations and Topological Properties," LEM Papers Series 2009/08, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    10. Su, Chi-Wei & Chang, Hsu-Ling & Chang, Tsangyao & Yin, Kedong, 2014. "Monetary convergence in East Asian countries relative to China," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 228-237.
    11. Ageliki Anagnostou & Ioannis Panteladis & Maria Tsiapa, 2015. "Disentangling different patterns of business cycle synchronicity in the EU regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 615-641, August.
    12. Miller, Marcus & Castrén, Olli & Zhang, Lei, 2005. "Capital flows and the US "New Economy": consumption smoothing and risk exposure," Working Paper Series 459, European Central Bank.
    13. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2018. "Debt Crisis in Europe (2001-2015): A Network General Equilibrium GVAR approach," MPRA Paper 89998, University Library of Munich, Germany.
    14. Eickmeier, Sandra, 2004. "Business Cycle Transmission from the US to Germany: a Structural Factor Approach," Discussion Paper Series 1: Economic Studies 2004,12, Deutsche Bundesbank.
    15. Gerhard Fenz & Martin Schneider, 2008. "Transmission of business cycle shocks between the US and the euro area," Working Papers 145, Oesterreichische Nationalbank (Austrian Central Bank).
    16. Pär Stockhammar & Pär Österholm, 2016. "Effects of US policy uncertainty on Swedish GDP growth," Empirical Economics, Springer, vol. 50(2), pages 443-462, March.
    17. Gefang Deborah & Strachan Rodney, 2009. "Nonlinear Impacts of International Business Cycles on the U.K. -- A Bayesian Smooth Transition VAR Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(1), pages 1-33, December.
    18. P�r Österholm & P�r Stockhammar, 2014. "The euro crisis and Swedish GDP growth - a study of spillovers," Applied Economics Letters, Taylor & Francis Journals, vol. 21(16), pages 1105-1110, November.
    19. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2018. "Debt dynamics in Europe: A Network General Equilibrium GVAR approach," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 175-202.
    20. Iulia Siedschlag, 2008. "Macroeconomic Differentials and Adjustment in the Euro Area," SUERF Studies, SUERF - The European Money and Finance Forum, number 2008/3 edited by Morten Balling, May.
    21. Eickmeier, Sandra & Moll, Katharina, 2008. "The global dimension of inflation: evidence from factor-augmented Phillips curves," Discussion Paper Series 1: Economic Studies 2008,16, Deutsche Bundesbank.
    22. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.
    23. Jiang, Chun & Jian, Na & Liu, Tie-Ying & Su, Chi-Wei, 2016. "Purchasing power parity and real exchange rate in Central Eastern European countries," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 349-358.
    24. Eickmeier, Sandra, 2009. "Analyse der Übertragung US-amerikanischer Schocks auf Deutschland auf Basis eines FAVAR," Working Papers 04/2009, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    25. Mastromarco Camilla & Laura Serlenga & Yongcheol Shin, 2013. "Globalisation and technological convergence in the EU," Journal of Productivity Analysis, Springer, vol. 40(1), pages 15-29, August.
    26. Erden, Lutfi & Ozkan, Ibrahim, 2014. "Determinants of international transmission of business cycles to Turkish economy," Economic Modelling, Elsevier, vol. 36(C), pages 383-390.
    27. Eric Girardin, 2004. "Regime-Dependent Synchronization of Growth Cycles between Japan and East Asia," Asian Economic Papers, MIT Press, vol. 3(3), pages 147-176.
    28. Stockhammar, Pär & Österholm, Pär, 2016. "The Impact of US Uncertainty Shocks on Small Open Economies," Working Papers 2016:5, Örebro University, School of Business.
    29. Miller, Marcus, 2005. "World Finance and the US 'New Economy': Risk Sharing and Risk Exposure," CEPR Discussion Papers 4855, C.E.P.R. Discussion Papers.
    30. Galvão, Ana Beatriz C., 2003. "Multivariate Threshold Models: TVARs and TVECMs," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(1), May.
    31. Michaelides, Panayotis G. & Papageorgiou, Theofanis, 2012. "On the transmission of economic fluctuations from the USA to EU-15 (1960–2011)," Journal of Economics and Business, Elsevier, vol. 64(6), pages 427-438.
    32. Buckle, Robert A. & Kim, Kunhong & Kirkham, Heather & McLellan, Nathan & Sharma, Jarad, 2007. "A structural VAR business cycle model for a volatile small open economy," Economic Modelling, Elsevier, vol. 24(6), pages 990-1017, November.
    33. Gabriele Tondl & Iulia Traistaru-Siedschlag, 2006. "Regional growth cycle synchronisation with the Euro Area," Papers WP173, Economic and Social Research Institute (ESRI).
    34. Metiu, Norbert & Hilberg, Björn & Grill, Michael, 2015. "Financial frictions and global spillovers," Discussion Papers 04/2015, Deutsche Bundesbank.
    35. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank.
    36. Marcus Miller & Olli Castrén & Lei Zhang, 2007. "'Irrational exuberance' and capital flows for the US New Economy: a simple global model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 89-105.
    37. Iulia Siedschlag & Gabriele Tondl, 2011. "Regional output growth synchronisation with the Euro Area," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 38(2), pages 203-221, May.
    38. Barrett, Alan & Bergin, Adele & FitzGerald, John & Traistaru-Siedschlag, Iulia, 2006. "Economic Assessment of the Euro Area: Forecasts and Policy Analysis, Autumn Report 2006," Research Series, Economic and Social Research Institute (ESRI), number sustat22.
    39. Calza Alessandro & Sousa João, 2006. "Output and Inflation Responses to Credit Shocks: Are There Threshold Effects in the Euro Area?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(2), pages 1-21, May.

  122. Oscar Jorda & Massimiliano Marcellino, 2003. "Time-Scale Transformations of Discrete-Time Processes," Working Papers 65, University of California, Davis, Department of Economics.

    Cited by:

    1. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
    2. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    3. Robert Kunst & Philip Franses, 2015. "Asymmetric time aggregation and its potential benefits for forecasting annual data," Empirical Economics, Springer, vol. 49(1), pages 363-387, August.
    4. Shigeru Fujita & Garey Ramey, 2006. "The cyclicality of job loss and hiring," Working Papers 06-17, Federal Reserve Bank of Philadelphia.

  123. George Kapetanios & Massimiliano Marcellino, 2003. "A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions," Working Papers 489, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Farmer, Roger & Henry, Jerome & Marcellino, Massimiliano & Beyer, Andreas, 2005. "Factor Analysis in a New-Keynesian Model," CEPR Discussion Papers 5266, C.E.P.R. Discussion Papers.
    2. Moneta, Fabio & Rüffer, Rasmus, 2006. "Business cycle synchronisation in East Asia," Working Paper Series 671, European Central Bank.
    3. Todd E. Clark, 2003. "Disaggregate evidence on the persistence of consumer price inflation," Research Working Paper RWP 03-11, Federal Reserve Bank of Kansas City.
    4. Aslanidis, Nektarios & Cipollini, Andrea, 2010. "Leading indicator properties of US high-yield credit spreads," Journal of Macroeconomics, Elsevier, vol. 32(1), pages 145-156, March.
    5. Darracq Pariès, Matthieu & Maurin, Laurent, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production: perspective from large panel factor models," Working Paper Series 894, European Central Bank.
    6. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank.
    7. Andrea Cipollini & George Kapetanios, 2005. "Forecasting Financial Crises and Contagion in Asia Using Dynamic Factor Analysis," Working Papers 538, Queen Mary University of London, School of Economics and Finance.
    8. George Kapetanios, 2004. "Dynamic Factor Extraction of Cross-Sectional Dependence in Panel Unit Root Tests," Working Papers 509, Queen Mary University of London, School of Economics and Finance.
    9. Eickmeier, Sandra, 2004. "Business Cycle Transmission from the US to Germany: a Structural Factor Approach," Discussion Paper Series 1: Economic Studies 2004,12, Deutsche Bundesbank.
    10. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    11. Cipollini, A. & Kapetanios, G., 2008. "A stochastic variance factor model for large datasets and an application to S&P data," Economics Letters, Elsevier, vol. 100(1), pages 130-134, July.
    12. Eickmeier, Sandra & Moll, Katharina, 2008. "The global dimension of inflation: evidence from factor-augmented Phillips curves," Discussion Paper Series 1: Economic Studies 2008,16, Deutsche Bundesbank.
    13. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "A Dynamic Factor Analysis of Business Cycle on Firm-Level Data," LEM Papers Series 2006/27, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    14. Nektarios Aslanidis & Andrea Cipollini, 2007. "Leading indicator properties of the US corporate spreads," Money Macro and Finance (MMF) Research Group Conference 2006 115, Money Macro and Finance Research Group.
    15. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    16. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    17. Andrea Cipollini & George Kapetanios, 2003. "A Dynamic Factor Analysis of Financial Contagion in Asia," Working Papers 498, Queen Mary University of London, School of Economics and Finance.
    18. Matthias Burgert & Stephane Dees, 2009. "Forecasting World Trade: Direct Versus “Bottom-Up” Approaches," Open Economies Review, Springer, vol. 20(3), pages 385-402, July.
    19. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank.

  124. Artis, Michael & Marcellino, Massimiliano & Proietti, Tommaso, 2003. "Dating the Euro Area Business Cycle," CEPR Discussion Papers 3696, C.E.P.R. Discussion Papers.

    Cited by:

    1. Douglas Sutherland & Peter Hoeller & Balázs Égert & Oliver Röhn, 2010. "Counter-cyclical Economic Policy," OECD Economics Department Working Papers 760, OECD Publishing.
    2. Don Harding & Adrian Pagan, 2006. "Measurement of Business Cycles," Department of Economics - Working Papers Series 966, The University of Melbourne.
    3. Hugues Pirotte & Celine Vaessen, 2008. "Residual value risk in the leasing industry: A European case," The European Journal of Finance, Taylor & Francis Journals, vol. 14(2), pages 157-177.
    4. Michael J. Artis & Jarko Fidrmuc & Johann Scharler, 2008. "The transmission of business cycles Implications for EMU enlargement1," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 16(3), pages 559-582, July.
    5. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Mattia Guerini & Duc Thi Luu & Mauro Napoletano, 2019. "Synchronization Patterns in the European Union," GREDEG Working Papers 2019-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    7. Franses, Philip Hans & Kunst, Robert M., 2007. "Analyzing a panel of seasonal time series: Does seasonality in industrial production converge across Europe?," Economic Modelling, Elsevier, vol. 24(6), pages 954-968, November.
    8. Skikiewicz Robert & Garczarczyk Józef, 2018. "Cyclical Fluctuations in the Banking Services Market and the Changes in the Situation of Entities from the Financial Services Sector," Central European Economic Journal, Sciendo, vol. 5(52), pages 118-129, January.
    9. Fabrizio Carmignani, 2009. "Endogenous optimal currency areas: The case of the Central African Economic and Monetary Community," Discussion Papers Series 390, School of Economics, University of Queensland, Australia.
    10. Emanuel Mönch & Harald Uhlig, 2005. "Towards a Monthly Business Cycle Chronology for the Euro Area," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 43-69.
    11. Pilar Bengoechea & Gabriel Pérez-Quirós, 2004. "A useful tool to identify recessions in the euro-area," Working Papers 0419, Banco de España.
    12. Valentina Aprigliano & Lorenzo Bencivelli, 2013. "Ita-coin: a new coincident indicator for the Italian economy," Temi di discussione (Economic working papers) 935, Bank of Italy, Economic Research and International Relations Area.
    13. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    14. Andreas Billmeier, 2009. "Ghostbusting: which output gap really matters?," International Economics and Economic Policy, Springer, vol. 6(4), pages 391-419, December.
    15. Jürgen Bierbaumer, 2012. "Regionale Konjunkturzyklen in Österreich," WIFO Monatsberichte (monthly reports), WIFO, vol. 85(11), pages 833-848, November.
    16. Klaus Abberger & Wolfgang Nierhaus, 2011. "ifo Geschäftsklima, Produktion und Ertragslage in der gewerblichen Wirtschaft," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(03), pages 21-24, February.
    17. Klaus Abberger, 2006. "Qualitative Business Surveys in Manufacturing and Industrial Production - What can be Learned from Industry Branch Results?," ifo Working Paper Series 31, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    18. Sumru Altuğ & Melike Bildirici, 2010. "Business Cycles around the Globe: A Regime Switching Approach," Working Papers 0032, Yildiz Technical University, Department of Economics, revised Mar 2010.
    19. Cukierman, Alex, 2007. "The Limits of Transparency," CEPR Discussion Papers 6475, C.E.P.R. Discussion Papers.
    20. João Loureiro & Manuel M. F. Martins & Ana Paula Ribeiro, 2009. "Cape Verde: The Case for Euroization," FEP Working Papers 317, Universidade do Porto, Faculdade de Economia do Porto.
    21. Monica Billio & Massimiliano Caporin & Guido Cazzavillan, 2008. "Dating EU15 monthly business cycle jointly using GDP and IPI," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(3), pages 333-366.
    22. Tommaso Proietti, 2009. "On the Model-Based Interpretation of Filters and the Reliability of Trend-Cycle Estimates," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 186-208.
    23. Mikael Bask & Jarko Fidrmuc, 2009. "Fundamentals and Technical Trading: Behavior of Exchange Rates in the CEECs," Open Economies Review, Springer, vol. 20(5), pages 589-605, November.
    24. Mario Cunha, 2010. "Modelling the Cyclical Behaviour of Wine Production in the Douro Region Using a Time-Varying Parameters Approach," Working Papers 2010.1, International Network for Economic Research - INFER.
    25. Maurizio Bovi, 2003. "Nonparametric Analysis Of The International Business Cycles," ISAE Working Papers 37, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    26. Jang, Tae-Seok & Sacht, Stephen, 2014. "Animal spirits and the business cycle: Empirical evidence from moment matching," Economics Working Papers 2014-06, Christian-Albrechts-University of Kiel, Department of Economics.
    27. Massmann, Michael & Mitchell, James, 2003. "Reconsidering the evidence: Are Eurozone business cycles converging," ZEI Working Papers B 05-2003, University of Bonn, ZEI - Center for European Integration Studies.
    28. Avouyi-Dovi, S. & Matheron, J., 2003. "Interactions between business cycles, stock market cycles and interest rates: the stylised facts," Financial Stability Review, Banque de France, issue 3, pages 80-99, November.
    29. Balázs Égert & Douglas Sutherland, 2014. "The Nature of Financial and Real Business Cycles: The Great Moderation and Banking Sector Pro-Cyclicality," Scottish Journal of Political Economy, Scottish Economic Society, vol. 61(1), pages 98-117, February.
    30. Peter Martey Addo & Monica Billio & Dominique Guegan, 2013. "Turning point chronology for the Euro-Zone: A Distance Plot Approach," Post-Print halshs-00803457, HAL.
    31. Mirko Abbritti; Sebastian Weber, 2008. "Labor Market Rigidities and the Business Cycle: Price vs. Quantity Restricting Institutions," IHEID Working Papers 01-2008, Economics Section, The Graduate Institute of International Studies, revised Jan 2008.
    32. Luis J. Álvarez & Alberto Cabrero, 2010. "Does housing really lead the business cycle?," Working Papers 1024, Banco de España.
    33. Klaus Abberger & Wolfgang Nierhaus, 2010. "Drei Monitorsysteme zur Analyse der sächsischen Industriekonjunktur," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 17(06), pages 33-39, December.
    34. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2008. "Business Cycles in the Euro Area," NBER Working Papers 14529, National Bureau of Economic Research, Inc.
    35. Juergen Bierbaumer-Polly, 2012. "Regional and Sectoral Business Cycles - Key Features for the Austrian economy," EcoMod2012 4074, EcoMod.
    36. Palenzuela, Diego Rodriguez & Saiz, Lorena & Stoevsky, Grigor & Tóth, Máté & Warmedinger, Thomas & Grigoraș, Veaceslav, 2024. "The euro area business cycle and its drivers," Occasional Paper Series 354, European Central Bank.
    37. Holtemöller, Oliver (Ed.) & Rahn, Jörg (Ed.) & Stierle, Michael H. (Ed.), 2009. "Characteristics of Business Cycles: Have they Changed?," IWH-Sonderhefte 5/2009, Halle Institute for Economic Research (IWH).
    38. Bengoechea, Pilar & Camacho, Maximo & Perez-Quiros, Gabriel, 2006. "A useful tool for forecasting the Euro-area business cycle phases," International Journal of Forecasting, Elsevier, vol. 22(4), pages 735-749.
    39. Mr. Andreas Billmeier, 2004. "Ghostbusting: Which Output Gap Measure Really Matters?," IMF Working Papers 2004/146, International Monetary Fund.
    40. Artis, Michael & Marcellino, Massimiliano & Proietti, Tommaso, 2004. "Characterizing the Business Cycle for Accession Countries," CEPR Discussion Papers 4457, C.E.P.R. Discussion Papers.
    41. V. Colombo, 2020. "Opening the Red Budget Box: Nonlinear Effects of a Tax Shock in the UK," Working Papers wp1142, Dipartimento Scienze Economiche, Universita' di Bologna.
    42. Michael Artis, 2003. "Is there a European Business Cycle?," CESifo Working Paper Series 1053, CESifo.
    43. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    44. Klaus Abberger & Wolfgang Nierhaus, 2008. "Die ifo Kapazitätsauslastung - ein gleichlaufender Indikator der deutschen Industriekonjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(16), pages 15-23, August.
    45. Jakob De Haan & Robert Inklaar & Richard Jong‐A‐Pin, 2008. "Will Business Cycles In The Euro Area Converge? A Critical Survey Of Empirical Research," Journal of Economic Surveys, Wiley Blackwell, vol. 22(2), pages 234-273, April.
    46. Monica Billio & Jacques Anas & Laurent Ferrara & Marco Lo Duca, 2007. "A turning point chronology for the Euro-zone," Working Papers 2007_33, Department of Economics, University of Venice "Ca' Foscari".
    47. Canova, Fabio & Ciccarelli, Matteo & Ortega, Eva, 2007. "Similarities and convergence in G-7 cycles," Journal of Monetary Economics, Elsevier, vol. 54(3), pages 850-878, April.
    48. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    49. Sanvi Avouyi-Dovi & Julien Matheron, 2005. "Interactions between business cycles, financial cycles and monetary policy: stylised facts," BIS Papers chapters, in: Bank for International Settlements (ed.), Investigating the relationship between the financial and real economy, volume 22, pages 273-98, Bank for International Settlements.
    50. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    51. Jacques Anas & Monica Billio & Laurent Ferrara & Gian Luigi Mazzi, 2008. "A System For Dating And Detecting Turning Points In The Euro Area," Manchester School, University of Manchester, vol. 76(5), pages 549-577, September.
    52. Jesus Crespo Cuaresma & Maria Antoinette Dimitz & Doris Ritzberger-Grünwald, 2002. "Growth, Convergence and EU Membership," Working Papers 62, Oesterreichische Nationalbank (Austrian Central Bank).
    53. Mr. Paul Cashin, 2004. "Caribbean Business Cycles," IMF Working Papers 2004/136, International Monetary Fund.
    54. Stijn Claessens & M. Ayhan Kose & Marco E. Terrones, 2009. "What happens during recessions, crunches and busts? [Business cycles for G-7 and European countries]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 24(60), pages 653-700.
    55. Maria Antoinette Silgoner & Jesús Crespo-Cuaresma & Gerhard Reitschuler, 2003. "The Fiscal Smile: The Effectiveness and Limits of Fiscal Stabilizers," IMF Working Papers 2003/182, International Monetary Fund.
    56. Favero, Carlo A. & Marcellino, Massimiliano, 2005. "Modelling and Forecasting Fiscal Variables for the euro Area," CEPR Discussion Papers 5294, C.E.P.R. Discussion Papers.
    57. Andrea Carriero & Massimiliano Marcellino, 2011. "Sectoral Survey‐based Confidence Indicators for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 175-206, April.
    58. Poplawski Ribeiro, Marcos & Beetsma, Roel, 2008. "The political economy of structural reforms under a deficit restriction," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 179-198, March.
    59. Pedro Cerqueira, 2011. "How Pervasive is the World Business Cycle?," Open Economies Review, Springer, vol. 22(1), pages 119-142, February.
    60. Erden, Lutfi & Ozkan, Ibrahim, 2014. "Determinants of international transmission of business cycles to Turkish economy," Economic Modelling, Elsevier, vol. 36(C), pages 383-390.
    61. Monica Billio & Jacques Anas & Laurent Ferrara & Marco Lo Duca, 2007. "Business Cycle Analysis with Multivariate Markov Switching Models," Working Papers 2007_32, Department of Economics, University of Venice "Ca' Foscari".
    62. Grech, Aaron George, 2013. "Adapting the Hodrick-Prescott Filter for Very Small Open Economies," MPRA Paper 48803, University Library of Munich, Germany.
    63. Pedro André Cerqueira, 2010. "A Closer Look at the World Business Cycle Synchronization," GEMF Working Papers 2010-21, GEMF, Faculty of Economics, University of Coimbra.
    64. Andrew Hallett & Christian Richter, 2006. "Measuring the Degree of Convergence among European Business Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 229-259, May.
    65. Wolfgang Nierhaus & Klaus Abberger, 2014. "Zur Prognose von konjunkturellen Wendepunkten: Dreimal-Regelversus Markov-Switching," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 67(16), pages 21-25, August.
    66. Klaus Abberger, 2005. "Ein Vergleich der Zeitreihen der Erzeugerpreise und der Preiserwartungen im ifo Konjunkturtest für das verarbeitende Gewerbe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 58(14), pages 50-51, July.
    67. Andrew Hughes Hallett & Christian R. Richter, 2007. "Time Varying Cyclical Analysis for Economies in Transition," CASE Network Studies and Analyses 0334, CASE-Center for Social and Economic Research.
    68. Bovi, M., 2005. "Economic Clubs and European Commitment. Evidence from the International Business Cycles," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(2), pages 101-122.
    69. Esteban Pérez Caldentey & Daniel Titelman & Pablo Carvallo, 2013. "Weak Expansions: A Distinctive Feature of the Business Cycle in Latin America and the Caribbean," Economics Working Paper Archive wp_749, Levy Economics Institute.
    70. Charles Amélie & Darné Olivier & Claude Diebolt, 2011. "A Revision of the US Business-Cycles Chronology 1790–1928," Working Papers 11-01, Association Française de Cliométrie (AFC).
    71. Peter Martey Addo & Monica Billio & Dominique Guegan, 2012. "Studies in Nonlinear Dynamics and Wavelets for Business Cycle Analysis," Documents de travail du Centre d'Economie de la Sorbonne 12023r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Nov 2013.
    72. Carlo Altavilla, 2004. "Do EMU Members Share the Same Business Cycle?," Journal of Common Market Studies, Wiley Blackwell, vol. 42(5), pages 869-896, December.
    73. Andrew Hughes Hallett & Christian Richter, 2009. "Is the US no longer the economy of first resort? Changing economic relationships in the Asia-Pacific region," International Economics and Economic Policy, Springer, vol. 6(2), pages 207-234, July.
    74. Artis, Michael, 2002. "Dating the Business Cycle in Britain," National Institute Economic Review, National Institute of Economic and Social Research, vol. 182, pages 90-95, October.
    75. Grech, Aaron George, 2014. "Investigating potential output using the Hodrick-Prescott filter: an application for Malta," MPRA Paper 57131, University Library of Munich, Germany.
    76. Willie Lahari, 2011. "Assessing Business Cycle Synchronisation - Prospects for a Pacific Islands Currency Union," Working Papers 1110, University of Otago, Department of Economics, revised Oct 2011.
    77. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88, May.
    78. Legrand, Romain, 2014. "Euro introduction: Has there been a structural change? Study on 10 European Union countries," Economic Modelling, Elsevier, vol. 40(C), pages 136-151.
    79. Vincent, BODART & Konstantin A., KHOLODILIN & Fati, SHADMAN-MEHTA, 2003. "Dating and Forecasting the Belgian Business Cycle," LIDAM Discussion Papers IRES 2003018, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    80. Luís Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2011. "Synchronization of Economic Sentiment Cycles in the Euro Area: a time-frequency analysis," CEF.UP Working Papers 1105, Universidade do Porto, Faculdade de Economia do Porto.
    81. Vítor Castro & Pedro A. Cerqueira & Rodrigo Martins, 2024. "Is There a Pervasive World Real Credit Cycle?," Open Economies Review, Springer, vol. 35(1), pages 99-119, February.
    82. Hasan Engin Duran, 2014. "Short-Run Dynamics of Income Disparities and Regional Cycle Synchronization in the U.S," Growth and Change, Wiley Blackwell, vol. 45(2), pages 292-332, June.
    83. Parnaudeau, Miia, 2008. "European Business Fluctuations in the Austrian Framework," MPRA Paper 25046, University Library of Munich, Germany.
    84. Willem Boshoff, 2005. "The properties of cycles in South African financial variables and their relation to the business cycle," Working Papers 02/2005, Stellenbosch University, Department of Economics.
    85. Proietti, Tommaso, 2005. "New algorithms for dating the business cycle," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 477-498, April.
    86. Klaus Abberger & Wolfgang Nierhaus, 2011. "Die aktuelle Wirtschaftsentwicklung im Lichte der ifo Konjunkturampel," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 36-38, November.

  125. Marcellino, Massimiliano, 2002. "Some Stylized Facts on Non-Systematic Fiscal Policy in the Euro Area," CEPR Discussion Papers 3635, C.E.P.R. Discussion Papers.

    Cited by:

    1. Luigi MARATTIN & Simone SALOTTI, 2010. "The Response of Private Consumption to Different Public Spending Categories:VAR Evidence from UK," EcoMod2010 259600111, EcoMod.
    2. Salotti, Simone & Marattin, Luigi, 2009. "On the usefulness of government spending in the EU area," MPRA Paper 19476, University Library of Munich, Germany.
    3. Richard Kneller & Florian Misch, 2017. "A Survey On The Output Effects Of Tax Reforms From A Policy Perspective," Contemporary Economic Policy, Western Economic Association International, vol. 35(1), pages 165-192, January.
    4. Francisco de Castro, 2006. "The macroeconomic effects of fiscal policy in Spain," Applied Economics, Taylor & Francis Journals, vol. 38(8), pages 913-924.
    5. Rilind Kabashi, 2017. "Macroeconomic effects of fiscal policy in the European Union, with particular reference to transition countries," Public Sector Economics, Institute of Public Finance, vol. 41(1), pages 39-69.
    6. Thomas A. Alexopoulos & Henry Thompson, 2021. "A macroeconomic simulation for Greece in the wake of its government debt crisis," Economic Change and Restructuring, Springer, vol. 54(3), pages 699-716, August.
    7. de Castro, Francisco & Hernández de Cos, Pablo, 2008. "The economic effects of fiscal policy: The case of Spain," Journal of Macroeconomics, Elsevier, vol. 30(3), pages 1005-1028, September.
    8. Jacopo Cimadomo & Agnès Bénassy-Quéré, 2012. "Changing Patterns of Fiscal Policy Multipliers in Germany, the UK and the US," PSE-Ecole d'économie de Paris (Postprint) hal-00966144, HAL.
    9. Ignacio Lozano & Karen Rodríguez, 2011. "Assessing the macroeconomic effects of fiscal policy in Colombia," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 3(3), pages 206-228, August.
    10. Ignacio Lozano Espitia & Karen Rodríguez, 2009. "Assessing the Macroeconomic Effects of Fiscal," Borradores de Economia 5386, Banco de la Republica.
    11. Enrico MARCHETTI & Francesco BUSATO & Bruno CHIARINI & Enrico MARCHETTI, 2010. "Indeterminacy, Underground Activities and Tax Evasion," EcoMod2010 259600112, EcoMod.
    12. Lorenzo Forni & Libero Monteforte & Luca Sessa, 2007. "The general equilibrium effects of fiscal policy: estimates for the euro area," Temi di discussione (Economic working papers) 652, Bank of Italy, Economic Research and International Relations Area.
    13. Nilsson, Kristian, 2008. "Conceptual Framework for Fiscal Policy," Occasional Papers 16, National Institute of Economic Research.
    14. Hasko, Harri, 2007. "Some unpleasant fiscal arithmetic: the role of monetary and fiscal policy in public debt dynamics since the 1970s," Bank of Finland Research Discussion Papers 28/2007, Bank of Finland.
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    4. Hwee Kwan Chow & Keen Meng Choy, 2008. "Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore," Economic Growth Centre Working Paper Series 0802, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
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    44. Michael Bleaney & Paul Mizen & Veronica Veleanu, 2012. "Bond Spreads as Predictors of Economic Activity in Eight European Economies," Discussion Papers 12/11, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    45. Hwee Kwan Chow & Keen Meng Choy, 2009. "Analyzing and Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore," Working Papers 05-2009, Singapore Management University, School of Economics.
    46. Angelini, Elena & Henry, Jerome & Marcellino, Massimiliano, 2006. "Interpolation and backdating with a large information set," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2693-2724, December.
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    49. Christian Gillitzer & Jonathan Kearns & Anthony Richards, 2005. "The Australian Business Cycle: A Coincident Indicator Approach," RBA Research Discussion Papers rdp2005-07, Reserve Bank of Australia.
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    53. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
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    85. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    86. António Rua, 2011. "A wavelet approach for factor‐augmented forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 666-678, November.
    87. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.
    88. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
    89. Victor Bystrov, 2006. "Forecasting Emerging Market Indicators: Brazil and Russia," Economics Working Papers ECO2006/12, European University Institute.
    90. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.
    91. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank.
    92. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    93. Barhoumi, K. & Darn , O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    94. Álvaro Aguirre R. & Luis Felipe Céspedes C., 2004. "Use of Dynamic Factor Analysis in Macroeconomic Forecasts," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(3), pages 35-46, December.
    95. Dias, Francisco & Pinheiro, Maximiano & Rua, António, 2015. "Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence," Economic Modelling, Elsevier, vol. 44(C), pages 266-272.

  127. Angelini, Henry, Marcellino, 2002. "interpolation with a large information set," Computing in Economics and Finance 2002 72, Society for Computational Economics.

    Cited by:

    1. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    2. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, "undated". "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Angelini, Elena & Marcellino, Massimiliano, 2007. "Econometric analyses with backdated data: unified Germany and the euro area," Working Paper Series 752, European Central Bank.

  128. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.

    Cited by:

    1. Stelios D. Bekiros & Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2016. "Dealing with Financial Instability under a DSGE modeling approach with Banking Intermediation: a predictability analysis versus TVP-VARs," Open Access publications 10197/7323, School of Economics, University College Dublin.
    2. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Open Access publications 10197/7588, School of Economics, University College Dublin.
    3. Emma SARNO & Alberto ZAZZARO, 2003. "Structural Convergence of Macroeconomic Time Series: Evidence for Inflation Rates in EU Countries," Working Papers 180, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    4. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
    5. Bekiros, Stelios D. & Paccagnini, Alessia, 2015. "Macroprudential Policy And Forecasting Using Hybrid Dsge Models With Financial Frictions And State Space Markov-Switching Tvp-Vars," Macroeconomic Dynamics, Cambridge University Press, vol. 19(7), pages 1565-1592, October.
    6. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    7. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    8. Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2008. "Forecasting euro area variables with German pre-EMU data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 465-481.
    9. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen Miller, 2010. "Forecasting Nevada Gross Gaming Revenue and Taxable Sales Using Coincident and Leading Employment Indexes," Working Papers 15-01, Eastern Mediterranean University, Department of Economics.
    10. Anindya Banerjee & Bill Russell, 2006. "A markup model for forecasting inflation for the euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(7), pages 495-511.
    11. Stelios Bekiros & Alessia Paccagnini, 2013. "Policy-oriented macroeconomic forecasting with hybrid DGSE and time-varying parameter VAR models," Working Papers 236, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
    12. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    13. Mihaela Simionescu, 2014. "What Type Of Social Capital Is Engaged By The French Dairy Stockbreeders? A Characterization Through Their Professional Identities," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 8(1), pages 87-102, JUNE.
    14. Bo Zhang & Joshua C.C. Chan & Jamie L. Cross, 2018. "Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts," CAMA Working Papers 2018-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Kauppi, Heikki & Virtanen, Timo, 2021. "Boosting nonlinear predictability of macroeconomic time series," International Journal of Forecasting, Elsevier, vol. 37(1), pages 151-170.
    16. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    17. Blerina Vika & Ilir Vika, 2021. "Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 10, September.
    18. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    19. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working papers 2012-38, University of Connecticut, Department of Economics, revised Dec 2013.
    20. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    21. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
    22. Heikki Kauppi & Timo Virtanen, 2018. "Boosting Non-linear Predictabilityof Macroeconomic Time Series," Discussion Papers 124, Aboa Centre for Economics.
    23. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Open Access publications 10197/7322, School of Economics, University College Dublin.
    24. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    25. Favero, Carlo A. & Marcellino, Massimiliano, 2005. "Modelling and Forecasting Fiscal Variables for the euro Area," CEPR Discussion Papers 5294, C.E.P.R. Discussion Papers.
    26. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    27. Kitov, Ivan, 2007. "Inflation, unemployment, labor force change in European countries," MPRA Paper 14557, University Library of Munich, Germany.
    28. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    29. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    30. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    31. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    32. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    33. Enders, Walter & Pascalau, Razvan, 2015. "Pretesting for multi-step-ahead exchange rate forecasts with STAR models," International Journal of Forecasting, Elsevier, vol. 31(2), pages 473-487.
    34. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, KSP Journals, vol. 3(3), pages 536-559, September.
    35. Paweł Baranowski, 2008. "Reguła Taylora i jej rozszerzenia," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 7-8, pages 1-23.
    36. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.
    37. Christos Avdoulas & Stelios Bekiros, 2018. "Nonlinear Forecasting of Euro Area Industrial Production Using Evolutionary Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 521-530, August.

  129. Banerjee, Anindya & Massimiliano Marcellino & Chiara Osbat, 2002. "Testing for PPP: Should We Use Panel Methods?," Royal Economic Society Annual Conference 2002 13, Royal Economic Society.

    Cited by:

    1. Christian Dreger & Hans-Eggert Reimers, 2006. "Hysteresis and Persistence in the Course of Unemployment: The EU and US Experience," Discussion Papers of DIW Berlin 572, DIW Berlin, German Institute for Economic Research.
    2. Kwaku Addai & Berna Serener & Dervis Kirikkaleli, 2022. "Asymmetricity in the Effect of Economic and Environmental Factors on Social Sustainability: Empirical Evidence from Eastern European Economies using Dynamic Analysis with CCEMG & D-H Causality Approac," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 12(3), pages 75-93.
    3. Marra, Alessandro & Colantonio, Emiliano, 2021. "The path to renewable energy consumption in the European Union through drivers and barriers: A panel vector autoregressive approach," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    4. Gilles Dufrénot & Valérie Mignon & Anne Péguin-Feissolle, 2007. "Testing the Finance-Growth Link: is There a Difference Between Developed and Developing Countries?," Working Papers 2007-24, CEPII research center.
    5. Belke, Ansgar & Dreger, Christian, 2011. "Current Account Imbalances in the Euro Area: Catching up or Competitiveness?," Ruhr Economic Papers 241, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Kaddour Hadri & Eiji Kurozumi & Yao Rao, 2014. "Novel Panel Cointegration Tests Emending for Cross-Section Dependence with N Fixed," Economics Working Papers 14-02, Queen's Management School, Queen's University Belfast.
    7. Sallahuddin Hassan & Zalila Othman & Mohd Zaini Abd Karim, 2011. "Private and Public Investment in Malaysia: A Panel Time-Series Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 1(4), pages 199-210.
    8. Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2006. "Panel Unit Root Tests and Spatial Dependence," Center for Policy Research Working Papers 88, Center for Policy Research, Maxwell School, Syracuse University.
    9. Werner, Daniel, 2013. "Regional convergence analysis for skill-specific employment groups," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79706, Verein für Socialpolitik / German Economic Association.
    10. Carl S. Bonham & Peter Fuleky & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii's Tourism Industry," Working Papers 201303, University of Hawaii at Manoa, Department of Economics.
    11. Ketenci, Natalya, 2013. "The Feldstein–Horioka puzzle in groupings of OECD members: A panel approach," Research in Economics, Elsevier, vol. 67(1), pages 76-87.
    12. Romero-Ávila, Diego & Usabiaga, Carlos, 2009. "The hypothesis of a unit root in OECD inflation revisited," Journal of Economics and Business, Elsevier, vol. 61(2), pages 153-161.
    13. Dang, Vinh Q.T. & So, Erin P.K. & Yang, Alan Yu & Chan, Kenneth S., 2020. "China and international market integration: Evidence from the law of one price in the Middle East and Africa," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    14. Christophe Hurlin & Valérie Mignon, 2006. "Une synthèse des tests de cointégration sur données de panel," Working Papers halshs-00070887, HAL.
    15. Amélie Charles & Olivier Darné & Jean-François Hoarau, 2010. "Does the real GDP per capita convergence hold in the Common Market for Eastern and Southern Africa?," Post-Print hal-00797485, HAL.
    16. Amornthum, Somchai & Bonham, Carl S., 2011. "Financial integration in the pacific basin region: RIP by PANIC attack?," Journal of International Money and Finance, Elsevier, vol. 30(6), pages 1019-1033, October.
    17. Everaert, Gerdie, 2014. "A panel analysis of the fisher effect with an unobserved I(1) world real interest rate," Economic Modelling, Elsevier, vol. 41(C), pages 198-210.
    18. Rafael González-Val & Luis Lanaspa & Fernando Sanz-Gracia, 2014. "New Evidence on Gibrat’s Law for Cities," Urban Studies, Urban Studies Journal Limited, vol. 51(1), pages 93-115, January.
    19. Josep Lluís Carrion-i-Silvestre & Vicente German-Soto, 2008. "Panel Data Stochastic Convergence Analysis of the Mexican Regions," IREA Working Papers 200805, University of Barcelona, Research Institute of Applied Economics, revised Apr 2008.
    20. Werner, Daniel, 2014. "New insights into the development of regional unemployment disparities," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100416, Verein für Socialpolitik / German Economic Association.
    21. Westerlund, Joakim & Breitung, Jörg, 2009. "Myths and Facts about Panel Unit Root Tests," Working Papers in Economics 380, University of Gothenburg, Department of Economics.
    22. Matteo Lanzafame, 2000. "The Nature of Regional Unemployment in Italy," Regional and Urban Modeling 283600051, EcoMod.
    23. Mauro Costantini & Claudio Lupi, 2013. "A Simple Panel-CADF Test for Unit Roots," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 276-296, April.
    24. Pedro M. G. Martins, 2010. "Aid Absorption and Spending in Africa: A Panel Cointegration Approach," Working Paper Series 1010, Department of Economics, University of Sussex Business School.
    25. Kappler, Marcus, 2006. "Panel Tests for Unit Roots in Hours Worked," ZEW Discussion Papers 06-022, ZEW - Leibniz Centre for European Economic Research.
    26. Hu, Yang & Valera, Harold Glenn A. & Oxley, Les, 2019. "Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework," Finance Research Letters, Elsevier, vol. 31(C), pages 138-145.
    27. Matteo Lanzafame, 2014. "Current account sustainability in advanced economies," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 23(7), pages 1000-1017, October.
    28. Imed Drine & Christophe Rault, 2008. "Purchasing Power Parity for Developing and Developed Countries. What can we Learn from Non-Stationary Panel Data Models?," CESifo Working Paper Series 2255, CESifo.
    29. Saadet Kasman & Adnan Kasman & Duygu Ayhan, 2010. "Testing the Purchasing Power Parity Hypothesis for the New Member and Candidate Countries of the European Union: Evidence from Lagrange Multiplier Unit Root Tests with Structural Breaks," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(2), pages 53-65, March.
    30. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.
    31. Syed A. Basher & Josep Lluis Carrión-i-Silvestre, 2008. "Deconstructing Shocks and Persistence in OECD Real Exchange Rates," Working Papers XREAP2008-06, Xarxa de Referència en Economia Aplicada (XREAP), revised Jun 2008.
    32. Jushan Bai & Serena Ng, 2001. "A PANIC Attack on Unit Roots and Cointegration," Boston College Working Papers in Economics 519, Boston College Department of Economics.
    33. Alfonso Arpaia & Alessandro Turrini, 2008. "Government expenditure and economic growth in the EU: long-run tendencies and short-term adjustment," European Economy - Economic Papers 2008 - 2015 300, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    34. Anindya Banerjee & Josep Lluís Carrion-i-Silvestre, 2006. "Cointegration in Panel Data with Breaks and Cross-Section Dependence," Economics Working Papers ECO2006/5, European University Institute.
    35. Christian Dreger, 2008. "Does the Nominal Exchange Rate Regime Affect the Real Interest Parity Condition?," Working Paper / FINESS 1.1c, DIW Berlin, German Institute for Economic Research.
    36. Tolga Omay & Mübariz Hasanov & Yongcheol Shin, 2018. "Testing for Unit Roots in Dynamic Panels with Smooth Breaks and Cross-Sectionally Dependent Errors," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 167-193, June.
    37. Ansgar Belke & Christian Dreger, 2013. "Current Account Imbalances in the Euro Area: Does Catching up Explain the Development?," Review of International Economics, Wiley Blackwell, vol. 21(1), pages 6-17, February.
    38. Tsangyao Chang & Ding Li & Yang-Cheng Lu & Chia-Hao Lee, 2011. "Purchasing power parity for East-Asia countries: further evidence based on panel stationary test with multiple structural breaks," Applied Economics, Taylor & Francis Journals, vol. 43(24), pages 3289-3298.
    39. Omri, Anis & Daly, Saida & Rault, Christophe & Chaibi, Anissa, 2015. "Financial development, environmental quality, trade and economic growth: What causes what in MENA countries," Energy Economics, Elsevier, vol. 48(C), pages 242-252.
    40. Jorg Breitung & Gianluca Cubadda, 2009. "Testing for cointegration in high-dimensional systems," CEIS Research Paper 148, Tor Vergata University, CEIS, revised 30 Sep 2009.
    41. Karolina Konopczak & Andrzej Torój, 2010. "Estimating the Baumol-Bowen and Balassa-Samuelson Effects in the Polish Economy - a Disaggregated Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(2), pages 117-150, March.
    42. Joakim, Westerlund & Johan, Blomquist, 2009. "Are Crime Rates Really Stationary?," Working Papers 2009:20, Lund University, Department of Economics.
    43. António AFONSO & Christophe RAULT, 2008. "What do we Really Know About Fiscal Sustainability in the EU? A Panel Data Diagnostic," EcoMod2008 23800000, EcoMod.
    44. Ogrokhina, Olena, 2019. "Persistence of prices in the Eurozone capital cities: Evidence from the Economist Intelligence Unit City Data," Economic Modelling, Elsevier, vol. 76(C), pages 330-338.
    45. Wagner, Martin, 2005. "On PPP, Unit Roots and Panels," Economics Series 176, Institute for Advanced Studies.
    46. Marcus Kappler, 2009. "Do hours worked contain a unit root? Evidence from panel data," Empirical Economics, Springer, vol. 36(3), pages 531-555, June.
    47. Mathilde Aubry & Jean Bonnet & Patricia Renou-Maissant, 2015. "Entrepreneurship and the business cycle: the “Schumpeter” effect versus the “refugee” effect—a French appraisal based on regional data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(1), pages 23-55, January.
    48. Holmes, M, 2004. "Nominal Exchange Rates Adjustment and Long-Run Competitiveness in Less Developed Countries," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 4(3).
    49. Tolga Omay & Muhammad Shahbaz & Chris Stewart, 2021. "Is there really hysteresis in the OECD unemployment rates? New evidence using a Fourier panel unit root test," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(4), pages 875-901, November.
    50. Bhavesh Garg & Pravakar Sahoo, 2021. "DO DIFFERENT TYPES OF CAPITAL INFLOWS HAVE DIFFERENTIAL IMPACT ON OUTPUT? Evidence from Time series and Panel Analysis," IEG Working Papers 443, Institute of Economic Growth.
    51. Claudia M. Buch & Paola Monti, 2008. "Openness and Income Dispaities: Does Trade Explain the 'Mezzogiorno' Effect?," IAW Discussion Papers 41, Institut für Angewandte Wirtschaftsforschung (IAW).
    52. Omay, Tolga & Shahbaz, Muhammad & Stewart, Chris, 2021. "Is There Really Hysteresis in OECD Countries’ Unemployment Rates? New Evidence Using a Fourier Panel Unit Root Test," MPRA Paper 107691, University Library of Munich, Germany, revised 10 May 2021.
    53. Tuomas Malinen, 2011. "Income Inequality and Savings: A Reassessment of the Relationship in Cointegrated Panels," DEGIT Conference Papers c016_076, DEGIT, Dynamics, Economic Growth, and International Trade.
    54. Jushan Bai & Serena Ng, 2001. "A New Look at Panel Testing of Stationarity and the PPP Hypothesis," Economics Working Paper Archive 467, The Johns Hopkins University,Department of Economics.
    55. Das, Samarjit & Bhattacharya, Kaushik, 2004. "Price Convergence across Regions in India," Bonn Econ Discussion Papers 1/2005, University of Bonn, Bonn Graduate School of Economics (BGSE).
    56. Mariam Camarero & Josep Lluis Carrion-i-Silvestre & Cecilio Tamarit, 2006. "New evidence of the real interest rate parity for OECD countries using panel unit root tests with breaks," Working Papers CREAP2006-14, Xarxa de Referència en Economia Aplicada (XREAP), revised Dec 2006.
    57. Megow, N. & Uetz, M.J. & Vredeveld, T., 2004. "Stochastic Online Scheduling on Parallel Machines," Research Memorandum 040, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    58. Syed A. Basher & Josep Lluís Carrion-i-Silvestre, 2007. "Another Look at the Null of Stationary RealExchange Rates. Panel Data with Structural Breaks and Cross-section Dependence," IREA Working Papers 200710, University of Barcelona, Research Institute of Applied Economics, revised May 2007.
    59. Mishra, Vinod & Sharma, Susan & Smyth, Russell, 2009. "Are fluctuations in energy consumption per capita transitory? Evidence from a panel of Pacific Island countries," Energy Policy, Elsevier, vol. 37(6), pages 2318-2326, June.
    60. Alessandro Crociata & Massimiliano Agovino & Donatella Furia & Giacomo Osmi & Nicola Mattoscio & Massimiliano Cerciello, 2020. "Impulse and time persistence of disaggregate welfare expenditure on growth in the EU," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(1), pages 13-38, April.
    61. Rafael González-Val & Marcos Sanso-Navarro, 2010. "Gibrat’s law for countries," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(4), pages 1371-1389, September.
    62. Valerija Botrić, 2010. "Foreign Direct Investments In Western Balkans: Privatization, Institutional Change And Banking Sector Dominance," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 55(187), pages 7-31, October –.
    63. Diego Romero-Ávila, 2007. "Unit roots and persistence in the nominal interest rate: a confirmatory analysis applied to the OECD," Canadian Journal of Economics, Canadian Economics Association, vol. 40(3), pages 980-1007, August.
    64. Nnyanzi John Bosco & Kilimani Nicholas & Oryema John Bosco, 2022. "How important are remittances to savings? Evidence from the Latin America and the Caribbean Countries," IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 13(1), pages 1-37, January.
    65. Olivier Damette & Philippe Frouté, 2010. "Is the crisis treatment exacerbating cautiousness or risk-taking? [Le traitement de la crise génère-t-il de la prudence ou de la prise de risque ?]," Post-Print hal-02336674, HAL.
    66. Konopczak, Karolina & Welfe, Aleksander, 2017. "Convergence-driven inflation and the channels of its absorption," Journal of Policy Modeling, Elsevier, vol. 39(6), pages 1019-1034.
    67. Pedro M G Martins, 2010. "Aid Absorption and Spending in Africa: A Panel Cointegration Approach," Discussion Papers 10/06, University of Nottingham, CREDIT.
    68. Charfeddine, Lanouar & Kahia, Montassar, 2019. "Impact of renewable energy consumption and financial development on CO2 emissions and economic growth in the MENA region: A panel vector autoregressive (PVAR) analysis," Renewable Energy, Elsevier, vol. 139(C), pages 198-213.
    69. Julián Ramajo Hernández(1) & Montserrat Ferré Carracedo(2), "undated". "Testing For Long-Run Purchasing Power Parity In The Post Bretton Woods Era: Evidence From Old And New Tests," Working Papers 24-05 Classification-JEL , Instituto de Estudios Fiscales.
    70. Lei Pan & Svetlana Maslyuk-Escobedo, 2019. "Stochastic convergence in per capita energy consumption and its catch-up rate: evidence from 26 African countries," Applied Economics, Taylor & Francis Journals, vol. 51(24), pages 2566-2590, May.
    71. Kawaljeet Kaur & Swati Mehta, 2023. "Modes of Technology Accumulation, Total Factor Productivity and Indian Manufacturing Sector: Firm-Level Analysis," Journal of South Asian Development, , vol. 18(1), pages 7-43, April.
    72. Yosra Saidi & Anis Ochi, 2023. "Estimating relationships among foreign direct investment, governance quality, and economic growth in developing countries using the threshold auto‐regressive model," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(2), pages 403-424, April.
    73. Natalya Ketenci, 2017. "The Effect of the European Union Customs Union on the Balance of Trade in Turkey," Foreign Trade Review, , vol. 52(4), pages 219-232, November.
    74. Chan, Tze-Haw & Baharumshah, Ahmad Zubaidi & Lau, Evan, 2005. "Real Financial Integration among the East Asian Economies: A SURADF Panel Approach," MPRA Paper 2021, University Library of Munich, Germany, revised Feb 2007.
    75. Grigoriev, A. & Sviridenko, M. & Uetz, M.J., 2005. "Machine scheduling with resource dependent processing times," Research Memorandum 050, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    76. Jeetendra Khadan & Amrita Deonarine, 2020. "Sustainability of current account balances in small states," Economics and Business Letters, Oviedo University Press, vol. 9(1), pages 14-20.
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    86. Bernard Fingleton, 2009. "Prediction Using Panel Data Regression with Spatial Random Effects," International Regional Science Review, , vol. 32(2), pages 195-220, April.
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    95. Klasen, Stephan & Herzer, Dierk & Nowak-Lehmann D., Felicitas, 2007. "In search of FDI-led growth in developing countries," Proceedings of the German Development Economics Conference, Göttingen 2007 14, Verein für Socialpolitik, Research Committee Development Economics.
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    10. Michael W. McCracken & Todd E. Clark, 2003. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Computing in Economics and Finance 2003 183, Society for Computational Economics.
    11. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
    12. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    13. Marcellino, Massimiliano, 2002. "Forecast Pooling for Short Time Series of Macroeconomic Variables," CEPR Discussion Papers 3313, C.E.P.R. Discussion Papers.
    14. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    15. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper series 49_07, Rimini Centre for Economic Analysis.
    16. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working papers 2012-38, University of Connecticut, Department of Economics, revised Dec 2013.
    17. Habrov, Vladimir, 2012. "Optimization of portfolio management based on vector autoregression models and multivariate volatility models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 28(4), pages 35-62.
    18. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    19. Heikki Kauppi & Timo Virtanen, 2018. "Boosting Non-linear Predictabilityof Macroeconomic Time Series," Discussion Papers 124, Aboa Centre for Economics.
    20. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    21. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    22. Favero, Carlo A. & Marcellino, Massimiliano, 2005. "Modelling and Forecasting Fiscal Variables for the euro Area," CEPR Discussion Papers 5294, C.E.P.R. Discussion Papers.
    23. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
    24. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    25. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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    28. Burns, Kelly & Moosa, Imad A., 2015. "Enhancing the forecasting power of exchange rate models by introducing nonlinearity: Does it work?," Economic Modelling, Elsevier, vol. 50(C), pages 27-39.
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    32. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
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    Cited by:

    1. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    2. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    3. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    4. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    5. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
    6. T. Ando & R. S. Tsay, 2009. "‘Model selection for generalized linear models with factor‐augmented predictors’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 243-246, May.
    7. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    8. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    9. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    10. Selen Baser Andic & Fethi Ogunc, 2015. "Variable Selection for Inflation : A Pseudo Out-of-sample Approach," Working Papers 1506, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    11. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    12. Barbara Rossi & Tatevik Sekhposyan, 2013. "Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set," Economics Working Papers 1370, Department of Economics and Business, Universitat Pompeu Fabra.
    13. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    14. Kajal Lahiri & Xuguang Sheng, 2008. "Measuring Forecast Uncertainty by Disagreement: The Missing Link," ifo Working Paper Series 60, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    15. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    16. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    17. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.
    18. Alonso Gomez & John M Maheu & Alex Maynard, 2008. "Improving Forecasts of Inflation using the Term Structure of Interest Rates," Working Papers tecipa-319, University of Toronto, Department of Economics.
    19. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    20. Juraj Hucek & Alexander Karsay & Marian Vavra, 2015. "Short-term Forecasting of Real GDP Using Monthly Data," Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.
    21. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    22. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    23. In Choi & Dukpa Kim & Yun Jung Kim & Noh-Sun Kwark, 2016. "A Multilevel Factor Model: Identification, Asymptotic Theory and Applications," Working Papers 1609, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    24. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
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    27. Ciccarelli, Matteo & Mojon, Benoît, 2006. "Global Inflation," Kiel Working Papers 1337, Kiel Institute for the World Economy (IfW Kiel).
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    29. Radoslaw Sobko & Maria Klonowska-Matynia, 2021. "The Relationship between the Purchasing Managers’ Index (PMI) and Economic Growth: The Case for Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 198-219.
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    31. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    32. Dovern, Jonas & Ziegler, Christina, 2008. "Predicting growth rates and recessions: assessing US leading indicators under real-time conditions," Kiel Working Papers 1397, Kiel Institute for the World Economy (IfW Kiel).
    33. Koivu, Tuuli & Mehrotra, Aaron & Nuutilainen, Riikka, 2008. "McCallum rule and Chinese monetary policy," BOFIT Discussion Papers 15/2008, Bank of Finland Institute for Emerging Economies (BOFIT).
    34. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
    35. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    36. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    37. Peter Vlaar & Ard den Reijer, 2004. "Forecasting inflation: An art as well as a science!," Computing in Economics and Finance 2004 148, Society for Computational Economics.
    38. Donadelli, Michael & Paradiso, Antonio & Riedel, Max, 2015. "A novel ex-ante leading indicator for the EU industrial production," SAFE Working Paper Series 118, Leibniz Institute for Financial Research SAFE.
    39. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    40. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    41. Johanna Garnitz & Robert Lehmann & Klaus Wohlrabe, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," CESifo Working Paper Series 7691, CESifo.
    42. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    43. Christian M. Dahl & Henrik Hansen & John Smidt, 2008. "The cyclical component factor model," CREATES Research Papers 2008-44, Department of Economics and Business Economics, Aarhus University.
    44. Thomas Flavin & Ekaterini Panopoulou & Theologos Pantelidis, 2009. "Forecasting growth and inflation in an enlarged euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 405-425.
    45. Chee Yin Yip & Hock Eam Lim & Hooi Hooi Lean, 2016. "Effectiveness of a Cluster of Determinants to Increase Economic Growth Rate: A Combined Statistical Criteria Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 728-735.
    46. Douglas Sutherland & Peter Hoeller & Rossana Merola & Volker Ziemann, 2012. "Debt and Macroeconomic Stability," OECD Economics Department Working Papers 1003, OECD Publishing.
    47. In Choi, 2012. "Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons," Working Papers 1209, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    48. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    49. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank.
    50. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2008. "Macroeconomic forecasting with matched principal components," International Journal of Forecasting, Elsevier, vol. 24(1), pages 87-100.
    51. Modugno, Michele, 2011. "Nowcasting inflation using high frequency data," Working Paper Series 1324, European Central Bank.
    52. Chris Brooks & Adrian Fernandez-Perez & Joëlle Miffre & Ogonna Nneji, 2014. "Commodity Risk Factors and the Cross-Section of Equity Returns," ICMA Centre Discussion Papers in Finance icma-dp2014-09, Henley Business School, University of Reading.
    53. Heij, C. & van Dijk, D.J.C. & Groenen, P.J.F., 2009. "Macroeconomic forecasting with real-time data: an empirical comparison," Econometric Institute Research Papers EI 2009-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    54. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    55. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    56. António Rua, 2016. "A wavelet-based multivariate multiscale approach for forecasting," Working Papers w201612, Banco de Portugal, Economics and Research Department.
    57. María Paula Bonel & Daniel J. Aromí, 2021. "Assessing GDP forecasts from autoregressive models: the impact of model complexity and training dataset," Asociación Argentina de Economía Política: Working Papers 4440, Asociación Argentina de Economía Política.
    58. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 27-42, March.
    59. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    60. de Bondt, Gabe J. & Hahn, Elke & Zekaite, Zivile, 2021. "ALICE: Composite leading indicators for euro area inflation cycles," International Journal of Forecasting, Elsevier, vol. 37(2), pages 687-707.
    61. Hilde C. Bjørnland & Leif Brubakk & Anne Sofie Jore, 2006. "Forecasting inflation with an uncertain output gap," Working Paper 2006/02, Norges Bank.
    62. Douglas Sutherland & Peter Hoeller, 2012. "Debt and Macroeconomic Stability: An Overview of the Literature and Some Empirics," OECD Economics Department Working Papers 1006, OECD Publishing.
    63. Travaglini, Guido, 2011. "Climate change: where is the hockey stick? evidence from millennial-scale reconstructed and updated temperature time series," MPRA Paper 35565, University Library of Munich, Germany.
    64. Dimitra Lamprou, 2015. "Nowcasting GDP in Greece: A Note on Forecasting Improvements from the Use of Bridge Models," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 13(1), pages 85-100.
    65. Haroon Khan, 2015. "The Impact of Oil and Gold Prices on the GDP Growth: Empirical Evidence from a Developing Country," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 1(11), pages 34-46, October.
    66. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    67. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    68. Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank.
    69. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    70. Donadelli, Michael & Paradiso, Antonio & Riedel, Max, 2016. "A quasi real-time leading indicator for the EU industrial production," SAFE Working Paper Series 118 [rev.], Leibniz Institute for Financial Research SAFE, revised 2016.
    71. Milena Lipovina-Božović, 2013. "A Comparison Of The Var Model And The Pc Factor Model In Forecasting Inflation In Montenegro," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 58(198), pages 115-136, July - Se.
    72. Bornali Bhandari & Samarth Gupta & Ajaya K. Sahu & K. S. Urs, 2021. "Business sentiments during India’s national lockdown: Lessons for second and potential third wave," Indian Economic Review, Springer, vol. 56(2), pages 335-350, December.
    73. Y. H. Venus Lun & John Carlton & Khaild Bichou, 2016. "Examining the economic impact of transport complex economies," Journal of Shipping and Trade, Springer, vol. 1(1), pages 1-17, December.
    74. Diron, Marie & Mojon, Benoît, 2005. "Forecasting the central bank's inflation objective is a good rule of thumb," Working Paper Series 564, European Central Bank.
    75. George Bagdatoglou & Alexandros Kontonikas & Mark E. Wohar, 2016. "Forecasting Us Inflation Using Dynamic General-To-Specific Model Selection," Bulletin of Economic Research, Wiley Blackwell, vol. 68(2), pages 151-167, April.
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    Cited by:

    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    2. Liang-chuan Wu & I-chan Tsai, 2014. "Three fuzzy goal programming models for index portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(8), pages 1155-1169, August.
    3. Cesarone, Francesco & Lampariello, Lorenzo & Sagratella, Simone, 2019. "A risk-gain dominance maximization approach to enhanced index tracking," Finance Research Letters, Elsevier, vol. 29(C), pages 231-238.
    4. Paskalis Glabadanidis, 2020. "Portfolio Strategies to Track and Outperform a Benchmark," JRFM, MDPI, vol. 13(8), pages 1-26, August.
    5. Jiang, Pan & Perez, M. Fabricio, 2021. "Follow the leader: Index tracking with factor models," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 337-350.
    6. Daniel Giamouridis & Sandra Paterlini, 2010. "Regular(Ized) Hedge Fund Clones," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(3), pages 223-247, September.
    7. Yu Zheng & Bowei Chen & Timothy M. Hospedales & Yongxin Yang, 2019. "Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach," Papers 1911.05052, arXiv.org, revised Nov 2019.
    8. Blitz, David & Huij, Joop, 2012. "Evaluating the performance of global emerging markets equity exchange-traded funds," Emerging Markets Review, Elsevier, vol. 13(2), pages 149-158.
    9. Erdemlioglu, Deniz & Joliet, Robert, 2019. "Long-term asset allocation, risk tolerance and market sentiment," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 1-19.
    10. Forni, Mario & Cavicchioli, Maddalena & Lippi, Marco & Zaffaroni, Paolo, 2016. "Eigenvalue Ratio Estimators for the Number of Common Factors," CEPR Discussion Papers 11440, C.E.P.R. Discussion Papers.
    11. Alexei Onatski & Chen Wang, 2021. "Spurious Factor Analysis," Econometrica, Econometric Society, vol. 89(2), pages 591-614, March.
    12. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," Working Papers ECARES ECARES 2011-019, ULB -- Universite Libre de Bruxelles.
    13. Paskalis Glabadanidis & Leon Zolotoy, 2013. "Benchmark replication portfolio strategies," Journal of Asset Management, Palgrave Macmillan, vol. 14(2), pages 95-110, April.
    14. Xiangyu Cui & Xuan Zhang, 2021. "Index tracking strategy based on mixed-frequency financial data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    15. Donatien Tafin Djoko & Yves Till�, 2015. "Selection of balanced portfolios to track the main properties of a large market," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 359-370, February.
    16. Derigs, Ulrich & Marzban, Shehab, 2009. "New strategies and a new paradigm for Shariah-compliant portfolio optimization," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1166-1176, June.
    17. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2023. "A flexible predictive density combination for large financial data sets in regular and crisis periods," Journal of Econometrics, Elsevier, vol. 237(2).
    18. Chang, Ya-Ting & Gau, Yin-Feng & Hsu, Chih-Chiang, 2017. "Liquidity Commonality in Foreign Exchange Markets During the Global Financial Crisis and the Sovereign Debt Crisis: Effects of Macroeconomic and Quantitative Easing Announcements," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 172-192.
    19. Reza Bradrania & Davood Pirayesh Neghab & Mojtaba Shafizadeh, 2022. "State-dependent stock selection in index tracking: a machine learning approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(1), pages 1-28, March.
    20. Boldin, Michael & Cici, Gjergji, 2010. "The index fund rationality paradox," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 33-43, January.
    21. Chuting Sun & Qi Wu & Xing Yan, 2023. "Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning," Papers 2301.07318, arXiv.org, revised Jan 2024.
    22. Haowen Bao & Yongmiao Hong & Yuying Sun & Shouyang Wang, 2024. "Sparse Interval-valued Time Series Modeling with Machine Learning," Papers 2411.09452, arXiv.org.
    23. Yu Zheng & Timothy M. Hospedales & Yongxin Yang, 2018. "Diversity and Sparsity: A New Perspective on Index Tracking," Papers 1809.01989, arXiv.org, revised Feb 2020.
    24. Sant’Anna, Leonardo R. & Filomena, Tiago P. & Caldeira, João F., 2017. "Index tracking and enhanced indexing using cointegration and correlation with endogenous portfolio selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 146-157.
    25. Sun, Chuting & Wu, Qi & Yan, Xing, 2024. "Dynamic CVaR portfolio construction with attention-powered generative factor learning," Journal of Economic Dynamics and Control, Elsevier, vol. 160(C).
    26. Wu, Dexiang & Dash Wu, Desheng, 2019. "An enhanced decision support approach for learning and tracking derivative index," Omega, Elsevier, vol. 88(C), pages 63-76.
    27. Aboura, Sofiane & Chevallier, Julien, 2017. "A new weighting-scheme for equity indexes," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 159-175.
    28. Canakgoz, N.A. & Beasley, J.E., 2009. "Mixed-integer programming approaches for index tracking and enhanced indexation," European Journal of Operational Research, Elsevier, vol. 196(1), pages 384-399, July.

  133. Marcellino, Massimiliano, 2002. "Forecast Pooling for Short Time Series of Macroeconomic Variables," CEPR Discussion Papers 3313, C.E.P.R. Discussion Papers.

    Cited by:

    1. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    2. Hilde C. Bjørnland & Karsten Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2012. "Does Forecast Combination Improve Norges Bank Inflation Forecasts?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 163-179, April.
    3. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
    4. Wagner Piazza Gaglianone & Luiz Renato Lima, 2014. "Constructing Optimal Density Forecasts From Point Forecast Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 736-757, August.
    5. Elliott, Graham & Timmermann, Allan, 2004. "Optimal forecast combinations under general loss functions and forecast error distributions," Journal of Econometrics, Elsevier, vol. 122(1), pages 47-79, September.
    6. Laura Carabotta & Peter Claeys, 2015. "Combine to compete: improving fiscal forecast accuracy over time," UB School of Economics Working Papers 2015/320, University of Barcelona School of Economics.
    7. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    8. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    9. Roberto S. Mariano & Suleyman Ozmucur, 2021. "Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 383-400, December.
    10. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    11. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    12. Aiolfi Marco & Capistrán Carlos & Timmermann Allan, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
    13. Marco Aiolfi & Carlo Ambrogio Favero, "undated". "Model Uncertainty, Thick Modelling and the predictability of Stock Returns," Working Papers 221, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    14. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper series 49_07, Rimini Centre for Economic Analysis.
    15. Mariola Pilatowska, 2009. "The Combined Forecasts Using the Akaike Weights," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 5-16.
    16. Favero, Carlo A. & Marcellino, Massimiliano, 2005. "Modelling and Forecasting Fiscal Variables for the euro Area," CEPR Discussion Papers 5294, C.E.P.R. Discussion Papers.
    17. David G. McMillan & Mark E. Wohar, 2010. "Stock return predictability and dividend-price ratio: a nonlinear approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 351-365.
    18. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    19. Benjamin Russo, 2010. "Is past prologue? Prospects for state and local sales tax bases," Applied Economics, Taylor & Francis Journals, vol. 42(18), pages 2261-2274.
    20. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
    21. Saeed Rasekhi, 2011. "Fundamental Modeling Exchange Rate using Genetic Algorithm: A Case Study of European Countries," Journal of Economics and Behavioral Studies, AMH International, vol. 3(6), pages 352-359.
    22. Antonio Musa, 2022. "Nowcasting Bosnia and Herzegovina GDP in Real Time," IHEID Working Papers 08-2022, Economics Section, The Graduate Institute of International Studies.
    23. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.

  134. Favero, Carlo A. & Marcellino, Massimiliano, 2001. "Large Datasets, Small Models and Monetary Policy in Europe," CEPR Discussion Papers 3098, C.E.P.R. Discussion Papers.

    Cited by:

    1. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
    2. Fabio Bagliano & Claudio Morana, 2008. "Factor vector autoregressive estimation: a new approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 3(1), pages 15-23, June.
    3. Marcellino, Massimiliano, 2002. "Some Stylized Facts on Non-Systematic Fiscal Policy in the Euro Area," CEPR Discussion Papers 3635, C.E.P.R. Discussion Papers.
    4. Reichlin, Lucrezia, 2002. "Factor Models in Large Cross-Sections of Time Series," CEPR Discussion Papers 3285, C.E.P.R. Discussion Papers.
    5. Hilde C. Bjørnland, 2006. "Monetary Policy and the Illusionary Exchange Rate Puzzle," Computing in Economics and Finance 2006 45, Society for Computational Economics.
    6. Belviso Francesco & Milani Fabio, 2006. "Structural Factor-Augmented VARs (SFAVARs) and the Effects of Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 6(3), pages 1-46, December.
    7. Mark J. Holmes, 2005. "Integration or Independence? An Alternative Assessment of Real Interest Rate Linkages in the European Union," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 34(3), pages 407-427, November.
    8. Julen Esteban‐Pretel & Elisa Faraglia, 2010. "Monetary Shocks in a Model with Skill Loss," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(7), pages 1235-1265, October.
    9. Guido Turnip, 2017. "Identification of Small Open Economy SVARs via Markov-Switching Heteroskedasticity," The Economic Record, The Economic Society of Australia, vol. 93(302), pages 465-483, September.
    10. Hae-shin Hwang & Woong Kim, 2012. "Estimation of Hybrid Phillips Curve: A Source of Conflicting Empirical Results," Southern Economic Journal, John Wiley & Sons, vol. 78(4), pages 1265-1288, April.
    11. Mark J. Holmes & Arthur Grimes, 2005. "Is there long-run convergence of regional house prices in the UK?," Working Papers 05_11, Motu Economic and Public Policy Research.
    12. Rebucci, Alessandro & Ciccarelli, Matteo, 2004. "Has the Transmission Mechanism of European Monetary Policy Changed in the Run-Up to EMU?," CEPR Discussion Papers 4535, C.E.P.R. Discussion Papers.
    13. Pappa, Evi & Molteni, Francesco, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," CEPR Discussion Papers 12541, C.E.P.R. Discussion Papers.
    14. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    15. Bjørnland, Hilde C., 2005. "Monetary policy and exchange rate interactions in a small open economy," Memorandum 31/2005, Oslo University, Department of Economics.
    16. Hilde C. Bjørnland, 2009. "Monetary policy and exchange rate overshooting: Dornbusch was right after all," Working Paper 2009/09, Norges Bank.
    17. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, "undated". "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    18. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    19. Francesco Daveri & Andrea Mascotto, "undated". "The IT revolution across the U.S. states," Working Papers 226, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    20. S. G. B Henry & A. R. Pagan, 2004. "The Econometrics of the New Keynesian Policy Model: Introduction," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(s1), pages 581-607, September.
    21. Fabio C. Bagliano & Claudio Morana, 2006. "A New Approach to Factor Vector Autoregressive Estimation with an Application to Large-Scale Macroeconometric Modelling," Carlo Alberto Notebooks 28, Collegio Carlo Alberto.
    22. Reichlin, Lucrezia & Sala, Luca & Giannone, Domenico, 2002. "Tracking Greenspan: Systematic and Unsystematic Monetary Policy Revisited," CEPR Discussion Papers 3550, C.E.P.R. Discussion Papers.
    23. Victor Bystrov, 2006. "Forecasting Emerging Market Indicators: Brazil and Russia," Economics Working Papers ECO2006/12, European University Institute.

  135. Banerjee, A. & Marcellino, M. & Osbat, C., 2000. "Some Cautions on the Use of Panel Methods for Integrated Series of Macro-economic Data," Economics Working Papers eco2000/20, European University Institute.

    Cited by:

    1. Christian Dreger & Hans-Eggert Reimers, 2006. "Hysteresis and Persistence in the Course of Unemployment: The EU and US Experience," Discussion Papers of DIW Berlin 572, DIW Berlin, German Institute for Economic Research.
    2. Thomas H. W. Ziesemer, 2023. "Semi-endogenous growth in a non-Walrasian DSEM for Brazil: estimation and simulation of changes in foreign income, human capital, R&D, and terms of trade," Economic Change and Restructuring, Springer, vol. 56(2), pages 1147-1183, April.
    3. Gilles Dufrénot & Valérie Mignon & Anne Péguin-Feissolle, 2007. "Testing the Finance-Growth Link: is There a Difference Between Developed and Developing Countries?," Working Papers 2007-24, CEPII research center.
    4. Belke, Ansgar & Dreger, Christian, 2011. "Current Account Imbalances in the Euro Area: Catching up or Competitiveness?," Ruhr Economic Papers 241, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Droge, Bernd & Örsal, Deniz Dilan Karaman, 2009. "Panel cointegration testing in the presence of a time trend," SFB 649 Discussion Papers 2009-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Kaddour Hadri & Eiji Kurozumi & Yao Rao, 2014. "Novel Panel Cointegration Tests Emending for Cross-Section Dependence with N Fixed," Economics Working Papers 14-02, Queen's Management School, Queen's University Belfast.
    7. Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2006. "Panel Unit Root Tests and Spatial Dependence," Center for Policy Research Working Papers 88, Center for Policy Research, Maxwell School, Syracuse University.
    8. Javier Emmanuel Anguiano-Pita & Antonio Ruiz-Porras, 2020. "Desarrollo financiero y crecimiento económico en América del Norte," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 12(1), pages 165-199, June.
    9. Riza Radmehr & Ernest Baba Ali & Samira Shayanmehr & Sayed Saghaian & Elham Darbandi & Ebenezer Agbozo & Samuel Asumadu Sarkodie, 2022. "Assessing the Global Drivers of Sustained Economic Development: The Role of Trade Openness, Financial Development, and FDI," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    10. Michel Cyrille Samba & Seabrook Arthur Mveng, 2023. "Substitution Between Private and Government Consumption in a Currency Area: The Case of the CFA Franc Zone," Public Finance Review, , vol. 51(3), pages 432-450, May.
    11. Werner, Daniel, 2013. "Regional convergence analysis for skill-specific employment groups," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79706, Verein für Socialpolitik / German Economic Association.
    12. G? Rehme, 2004. "Endogenous Policy and Cross-Country Growth Empirics," Econometric Society 2004 North American Summer Meetings 262, Econometric Society.
    13. Gabriel Bruneau & Kevin Moran, 2017. "Exchange rate fluctuations and labour market adjustments in Canadian manufacturing industries," Canadian Journal of Economics, Canadian Economics Association, vol. 50(1), pages 72-93, February.
    14. Ana G. Bus y José L. Nicolini-Llosa, 2015. "La renta diferencial agrícola en Argentina en 1986-2008, con datos de panel y co-integración," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 61, pages 53-79, January-D.
    15. Carl S. Bonham & Peter Fuleky & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii's Tourism Industry," Working Papers 201303, University of Hawaii at Manoa, Department of Economics.
    16. Ketenci, Natalya, 2013. "The Feldstein–Horioka puzzle in groupings of OECD members: A panel approach," Research in Economics, Elsevier, vol. 67(1), pages 76-87.
    17. Melisso Boschi & Alessandro Girardi, 2008. "The contribution of domestic, regional, and international factors to Latin America’s business cycle," ISAE Working Papers 105, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    18. Karikallio, Hanna, 2015. "Cross-commodity Price Transmission and Integration of the EU Livestock Market of Pork and Beef: Panel Time-series Approach," 2015 Conference, August 9-14, 2015, Milan, Italy 211832, International Association of Agricultural Economists.
    19. Chan, Tze-Haw & Baharumshah, Ahmad Zubaidi, 2012. "Financial Integration between China and Asia Pacific Trading Partners: Parities Evidence from the First- and Second-generation Panel Tests," MPRA Paper 37801, University Library of Munich, Germany.
    20. Francesca Iorio & Stefano Fachin, 2014. "Savings and investments in the OECD: a panel cointegration study with a new bootstrap test," Empirical Economics, Springer, vol. 46(4), pages 1271-1300, June.
    21. Gengenbach, C. & Urbain, J.R.Y.J. & Westerlund, J., 2008. "Panel error correction testing with global stochastic trends," Research Memorandum 051, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    22. de Bandt, Olivier & Banerjee, Anindya & Kozluk, Tomasz, 2007. "Measuring Long-Run Exchange Rate Pass-Through," Economics Discussion Papers 2007-32, Kiel Institute for the World Economy (IfW Kiel).
    23. Dang, Vinh Q.T. & So, Erin P.K. & Yang, Alan Yu & Chan, Kenneth S., 2020. "China and international market integration: Evidence from the law of one price in the Middle East and Africa," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    24. Karaman Örsal, Deniz Dilan & Droge, Bernd, 2014. "Panel cointegration testing in the presence of a time trend," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 377-390.
    25. Lars Sorge & Anne Neumann, 2019. "The Impact of Population, Affluence, Technology, and Urbanization on CO2 Emissions across Income Groups," Discussion Papers of DIW Berlin 1812, DIW Berlin, German Institute for Economic Research.
    26. Rehme, G¸nther, 2002. "Why Run a Million Regressions? Endogenous Policy and Cross-Country Growth Empirics," Royal Economic Society Annual Conference 2002 157, Royal Economic Society.
    27. Felicitas Nowak-Lehmann D. & Inmaculada Martínez Zarzoso & Stephan Klasen & Dierk Herzer, 2009. "Aid and Trade - A Donor’s Perspective," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 7, Courant Research Centre PEG.
    28. Christophe Hurlin & Valérie Mignon, 2006. "Une synthèse des tests de cointégration sur données de panel," Working Papers halshs-00070887, HAL.
    29. Carrion-i-Silvestre Josep Lluis & Surdeanu Laura, 2011. "Panel Cointegration Rank Testing with Cross-Section Dependence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-43, September.
    30. Syed Basher & Andrea Masini & Sam Aflaki, 2014. "Time Series Properties of the Renewable Energy Diffusion Process: Implications for Energy Policy Design and Assessment," Working Papers hal-02018566, HAL.
    31. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2010. "Forecasting with Factor-augmented Error Correction Models," CEPR Discussion Papers 7677, C.E.P.R. Discussion Papers.
    32. Mariam Camarero & Inmaculada Martínez-Zarzoso & Felicitas Nowak-Lehmann & Cecilio Tamarit, 2016. "Trade Openness and Income: A Tale of Two Regions," The World Economy, Wiley Blackwell, vol. 39(3), pages 386-408, March.
    33. Everaert, Gerdie, 2014. "A panel analysis of the fisher effect with an unobserved I(1) world real interest rate," Economic Modelling, Elsevier, vol. 41(C), pages 198-210.
    34. Tolga Omay & Mubariz Hasanov & Nuri Uçar, 2012. "Energy Consumption and Economic Growth: Evidence from Nonlinear Panel Cointegration and Causality Tests," Hacettepe University Department of Economics Working Papers 20130, Hacettepe University, Department of Economics.
    35. Bhattacharya, Mita & Narayan, Paresh, 2015. "Output and labor productivity in organized manufacturing: A panel cointegration analysis for India," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 171-177.
    36. Christian Dreger & Dierk Herzer, 2013. "A further examination of the export-led growth hypothesis," Empirical Economics, Springer, vol. 45(1), pages 39-60, August.
    37. Josep Lluís Carrion-i-Silvestre & Vicente German-Soto, 2008. "Panel Data Stochastic Convergence Analysis of the Mexican Regions," IREA Working Papers 200805, University of Barcelona, Research Institute of Applied Economics, revised Apr 2008.
    38. Werner, Daniel, 2014. "New insights into the development of regional unemployment disparities," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100416, Verein für Socialpolitik / German Economic Association.
    39. Uzar, Umut & Eyuboglu, Kemal & Akdag, Saffet & Alola, Andrew Adewale, 2023. "Causal inference of financial development and institutional quality across the globe," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    40. Ziesemer, Thomas, 2019. "Can we have growth when population is stagnant? Testing linear growth rate formulas and their cross-unit cointegration of non-scale endogenous growth models," MERIT Working Papers 2019-021, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    41. Uzar, Umut, 2020. "Political economy of renewable energy: Does institutional quality make a difference in renewable energy consumption?," Renewable Energy, Elsevier, vol. 155(C), pages 591-603.
    42. Westerlund, Joakim & Costantini, Mauro, 2006. "Panel Cointegration and the Neutrality of Money," Working Papers 2006:18, Lund University, Department of Economics.
    43. Cecilio R. Tamarit Escalona & Estrella Gómez, 2011. "The euro effect on trade: evidence in gravity equations using panel cointegration techniques," Working Papers. Serie EC 2011-07, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    44. Mauro Costantini & Claudio Lupi, 2013. "A Simple Panel-CADF Test for Unit Roots," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 276-296, April.
    45. Betty Daniel & Christos Shiamptanis, 2012. "Pushing the Limit? Fiscal Policy in the European Monetary Union," Working Papers 033, Toronto Metropolitan University, Department of Economics.
    46. Pedro M. G. Martins, 2010. "Aid Absorption and Spending in Africa: A Panel Cointegration Approach," Working Paper Series 1010, Department of Economics, University of Sussex Business School.
    47. Vishal Chandr Jaunky & Robert Lundmark, 2015. "Are Shocks to Wood Fuel Production Permanent? Evidence from the EU," Energies, MDPI, vol. 8(11), pages 1-11, November.
    48. Richard G. Anderson & Hailong Qian & Robert H. Rasche, 2006. "Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators," Working Papers 2006-050, Federal Reserve Bank of St. Louis.
    49. Mariam Camarero & Estrella Gómez & Cecilio Tamarit, 2012. "The euro impact on trade. Long run evidence with structural breaks," ThE Papers 10/27, Department of Economic Theory and Economic History of the University of Granada..
    50. Kula Ferit & Aslan Alper, 2010. "Hysteresis vs. Natural Rate of Unemployment: One, the Other, or Both?," South East European Journal of Economics and Business, Sciendo, vol. 5(1), pages 91-94, April.
    51. Pawel Dlotko & Simon Rudkin & Wanling Qiu, 2019. "Topologically Mapping the Macroeconomy," Papers 1911.10476, arXiv.org.
    52. Jin, Taeyoung, 2022. "Impact of heat and electricity consumption on energy intensity: A panel data analysis," Energy, Elsevier, vol. 239(PA).
    53. Imed Drine & Christophe Rault, 2008. "Purchasing Power Parity for Developing and Developed Countries. What can we Learn from Non-Stationary Panel Data Models?," CESifo Working Paper Series 2255, CESifo.
    54. Burret, Heiko T. & Feld, Lars P. & Köhler, Ekkehard A., 2015. "(Un-)Sustinability of public finances in German Laender: A panel time series approach," Freiburg Discussion Papers on Constitutional Economics 15/09, Walter Eucken Institut e.V..
    55. Castagnetti, Carolina, 2018. "A novel approach for testing the parity relationship between CDS and credit spread," Economics Letters, Elsevier, vol. 172(C), pages 115-117.
    56. G.S. Chen & Y. Yao & Julien Malizard, 2017. "Does foreign direct investment crowd in or crowd out private domestic investment in China? The effect of entry mode," Post-Print hal-03124847, HAL.
    57. Jaap W. B. Bos & Bertrand Candelon & Claire Economidou, 2016. "Does knowledge spill over across borders and technology regimes?," Journal of Productivity Analysis, Springer, vol. 46(1), pages 63-82, August.
    58. Catherine Bac & Yannick le Pen, 2002. "An International Comparison of Health Care Expenditure Determinants," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C5-1, International Conferences on Panel Data.
    59. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.
    60. Syed A. Basher & Josep Lluis Carrión-i-Silvestre, 2008. "Deconstructing Shocks and Persistence in OECD Real Exchange Rates," Working Papers XREAP2008-06, Xarxa de Referència en Economia Aplicada (XREAP), revised Jun 2008.
    61. Alfonso Arpaia & Alessandro Turrini, 2008. "Government expenditure and economic growth in the EU: long-run tendencies and short-term adjustment," European Economy - Economic Papers 2008 - 2015 300, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    62. Anindya Banerjee & Josep Lluís Carrion-i-Silvestre, 2006. "Cointegration in Panel Data with Breaks and Cross-Section Dependence," Economics Working Papers ECO2006/5, European University Institute.
    63. Christian Dreger, 2008. "Does the Nominal Exchange Rate Regime Affect the Real Interest Parity Condition?," Working Paper / FINESS 1.1c, DIW Berlin, German Institute for Economic Research.
    64. Sarah M. Lein & Thomas Maag, 2008. "The Formation of Inflation Perceptions - Some Empirical Facts for European Countries," KOF Working papers 08-204, KOF Swiss Economic Institute, ETH Zurich.
    65. Tolga Omay & Mübariz Hasanov & Yongcheol Shin, 2018. "Testing for Unit Roots in Dynamic Panels with Smooth Breaks and Cross-Sectionally Dependent Errors," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 167-193, June.
    66. Ansgar Belke & Christian Dreger, 2013. "Current Account Imbalances in the Euro Area: Does Catching up Explain the Development?," Review of International Economics, Wiley Blackwell, vol. 21(1), pages 6-17, February.
    67. Günther Rehme, 2002. "(Re-)Distribution of Personal Incomes, Education and Economic Performance Across Countries," CESifo Working Paper Series 711, CESifo.
    68. Bonizzi, Bruno, 2017. "Institutional investors’ allocation to emerging markets: A panel approach to asset demand," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 47-64.
    69. João Sousa Andrade, 2006. "Mobilidade do Capital e Sustentabilidade Externa: uma aplicação da tese de F-H a Portugal (1910-2004)," GEMF Working Papers 2006-04, GEMF, Faculty of Economics, University of Coimbra.
    70. Iorio, Francesca Di & Fachin, Stefano, 2014. "Savings and investments in the OECD, 1970–2007: A test of panel cointegration with regime changes," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 59-76.
    71. Westerlund, Joakim & Edgerton , David, 2005. "Panel Cointegration Tests with Deterministic Trends and Structural Breaks," Working Papers 2005:42, Lund University, Department of Economics.
    72. Brantley Liddle, 2013. "The Energy, Economic Growth, Urbanization Nexus Across Development: Evidence from Heterogeneous Panel Estimates Robust to Cross-Sectional Dependence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    73. António AFONSO & Christophe RAULT, 2008. "What do we Really Know About Fiscal Sustainability in the EU? A Panel Data Diagnostic," EcoMod2008 23800000, EcoMod.
    74. Cameron Fen & Samir Undavia, 2022. "Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model," Papers 2203.06540, arXiv.org.
    75. Mathilde Aubry & Jean Bonnet & Patricia Renou-Maissant, 2015. "Entrepreneurship and the business cycle: the “Schumpeter” effect versus the “refugee” effect—a French appraisal based on regional data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(1), pages 23-55, January.
    76. Beckmann, Joscha & Belke, Ansgar & Czudaj, Robert, 2014. "Does global liquidity drive commodity prices?," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 224-234.
    77. Sven Steinkamp & Frank Westermann, 2012. "On Creditor Seniority and Sovereign Bond Prices in Europe," IEER Working Papers 92, Institute of Empirical Economic Research, Osnabrueck University, revised 25 Jul 2017.
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    1. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
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    6. Isiklar, Gultekin & Lahiri, Kajal, 2007. "How far ahead can we forecast? Evidence from cross-country surveys," International Journal of Forecasting, Elsevier, vol. 23(2), pages 167-187.
    7. Elena Gennari & Raffaela Giordano & Sandro Momigliano, 2005. "Dealing with Unexpected Shocks to the Budget," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 61(2), pages 201-219, July.
    8. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
    9. João Tovar Jalles, 2020. "European commission’s fiscal forecasts in CEE countries: a thorough assessment," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 23(2), pages 161-183, July.
    10. António Afonso & Jorge Silva, 2012. "The Fiscal Forecasting Track Record of the European Commission and Portugal," Working Papers Department of Economics 2012/37, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    11. Rybacki, Jakub, 2019. "Fiscal Deficit Forecasts by International Institutions: Evidence for a Double Standard?," MPRA Paper 94031, University Library of Munich, Germany.
    12. Carabotta, Laura & Paluzie, Elisenda & Ramos, Raul, 2017. "Does fiscal responsibility matter? Evidence from public and private forecasters in Italy," International Journal of Forecasting, Elsevier, vol. 33(3), pages 694-706.
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    86. Cronin, David & McInerney, Niall, 2023. "Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment," European Journal of Political Economy, Elsevier, vol. 76(C).
    87. Jalles, João Tovar & Karibzhanov, Iskander & Loungani, Prakash, 2015. "Cross-country evidence on the quality of private sector fiscal forecasts," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 186-201.
    88. Heiko T. Burret & Lars P. Feld & Kö & Ekkehard A. hler, 2014. "Panel Cointegration Tests on the Fiscal Sustainability of German Laender," CESifo Working Paper Series 4929, CESifo.
    89. Diego J. Pedregal & Javier J. Pérez & Antonio Sánchez Fuentes, 2014. "A Tookit to strengthen Government," Hacienda Pública Española / Review of Public Economics, IEF, vol. 211(4), pages 117-146, December.
    90. Sabaj, Ernil & Kahveci, Mustafa, 2018. "Forecasting tax revenues in an emerging economy: The case of Albania," MPRA Paper 84404, University Library of Munich, Germany.
    91. Alexander, Marcus & Christakis, Nicholas A., 2008. "Bias and asymmetric loss in expert forecasts: A study of physician prognostic behavior with respect to patient survival," Journal of Health Economics, Elsevier, vol. 27(4), pages 1095-1108, July.
    92. de Mendonça, Helder Ferreira & de Deus, Joseph David Barroso Vasconcelos, 2019. "Central bank forecasts and private expectations: An empirical assessment from three emerging economies," Economic Modelling, Elsevier, vol. 83(C), pages 234-244.
    93. Massimiliano Marcellino, 2004. "Forecast Pooling for European Macroeconomic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 91-112, February.

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    1. Leal, Teresa & Pérez, Javier J. & Tujula, Mika & Vidal, Jean-Pierre, 2007. "Fiscal forecasting: lessons from the literature and challenges," Working Paper Series 843, European Central Bank.
    2. Fabrizio Balassone & Maura Francese & Stefania Zotteri, 2010. "Cyclical asymmetry in fiscal variables in the EU," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(4), pages 381-402, November.
    3. Kinnunen, Helvi & Kuoppamäki, Pasi, 1998. "Sustainability of public finances in Finland and the four largest Euro-area Economies," Bank of Finland Research Discussion Papers 25/1998, Bank of Finland.
    4. Carabotta, Laura & Paluzie, Elisenda & Ramos, Raul, 2017. "Does fiscal responsibility matter? Evidence from public and private forecasters in Italy," International Journal of Forecasting, Elsevier, vol. 33(3), pages 694-706.
    5. Cosimo Magazzino & Francesco Forte & Lorenzo Giolli, 2022. "On the Italian public accounts' sustainability: A wavelet approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 943-952, January.
    6. Merola, Rossana & Pérez, Javier J., 2014. "Fiscal Forecast Errors: Governments Versus Independent Agencies?," Papers RB2014/1/1, Economic and Social Research Institute (ESRI).
    7. Fabrizio Balassone & Maura Francese & Stefania Zotteri, 2008. "Cyclical asymmetry in fiscal variables," Temi di discussione (Economic working papers) 671, Bank of Italy, Economic Research and International Relations Area.
    8. Huseyin Kalyoncu, 2005. "Fiscal policy sustainability: test of intertemporal borrowing constraints," Applied Economics Letters, Taylor & Francis Journals, vol. 12(15), pages 957-962.
    9. Gordon L. Brady & Cosimo Magazzino, 2018. "Sustainability and comovement of government debt in EMU Countries: A panel data analysis," Southern Economic Journal, John Wiley & Sons, vol. 85(1), pages 189-202, July.
    10. Peter Claeys, 2007. "Sustainability of EU fiscal policies, a panel test," IREA Working Papers 200702, University of Barcelona, Research Institute of Applied Economics, revised Jan 2007.
    11. Magazzino, Cosimo & Brady, Gordon L. & Forte, Francesco, 2019. "A panel data analysis of the fiscal sustainability of G-7 countries," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    12. Alberto Bagnai, 2004. "Keynesian And Neoclassical Fiscal Sustainability Indicators, With Applications To Emu Member Countries," Public Economics 0411005, University Library of Munich, Germany.
    13. Chinn, Menzie David & Frankel, Jeffrey A., 2003. "The Euro Area and World Interest Rates," Santa Cruz Department of Economics, Working Paper Series qt2nb2h4zr, Department of Economics, UC Santa Cruz.
    14. Laura Carabotta, 2014. "Which Agency and Which Period is The Best? Analyzing National and International Fiscal Forecasts in Italy," International Journal of Economic Sciences, Prague University of Economics and Business, vol. 2014(1), pages 27-46.
    15. Wolfgang Kitterer, 2007. "Nachhaltige Finanz‐ und Investitionspolitik der Bundesländer," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 8(S1), pages 53-83, April.
    16. Fabrizio Balassone & Maura Francese, 2004. "Cyclical asymmetry in fiscal policy, debt accumulation and the Treaty of Maastricht," Temi di discussione (Economic working papers) 531, Bank of Italy, Economic Research and International Relations Area.
    17. Gerrit B. Koester & Christoph Priesmeier, 2013. "Does Wagner´s Law Ruin the Sustainability of German Public Finances?," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 69(3), pages 256-288, September.
    18. Gordon L. Brady & Cosimo Magazzino, 2018. "Fiscal Sustainability in the EU," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 46(3), pages 297-311, September.
    19. Strauch, Rolf, 1999. "Monitoring fiscal adjustments in the European Union and EMU," Discussion Paper Series 1: Economic Studies 1999,04, Deutsche Bundesbank.
    20. Bravo, Ana Bela Santos & Silvestre, Antonio Luis, 2002. "Intertemporal sustainability of fiscal policies: some tests for European countries," European Journal of Political Economy, Elsevier, vol. 18(3), pages 517-528, September.
    21. Michał Mackiewicz, 2021. "The sustainability of fiscal policy in southern African countries–a comparative empirical perspective," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 18(2), pages 337-350, April.
    22. Cosimo Magazzino & Marco Mele, 2022. "A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(2), pages 289-338, July.
    23. Michael J. Artis & Marco Buti, 2000. "Close to Balance or in Surplus. A Policy Maker’s Guide to the Implementation of the Stability and Growth Pact," EUI-RSCAS Working Papers 28, European University Institute (EUI), Robert Schuman Centre of Advanced Studies (RSCAS).
    24. António Afonso, 2005. "Fiscal Sustainability: The Unpleasant European Case," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 61(1), pages 19-44, March.
    25. Cosimo Magazzino & Mihai Mutascu, 2019. "A wavelet analysis of Italian fiscal sustainability," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-13, December.
    26. Bystrov, Victor & Mackiewicz, Michał, 2020. "Recurrent explosive public debts and the long-run fiscal sustainability," Journal of Policy Modeling, Elsevier, vol. 42(2), pages 437-450.
    27. Javier Gómez Biscarri & Fernando Pérez de Gracia, 2002. "Stock Market Cycles and Stock Market Development in Spain," Faculty Working Papers 03/02, School of Economics and Business Administration, University of Navarra.
    28. António Afonso, 2000. "Fiscal policy sustainability: some unpleasant European evidence," Working Papers Department of Economics 2000/12, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    29. Cunado, J. & Gil-Alana, L. A. & Perez de Gracia, F., 2004. "Is the US fiscal deficit sustainable?: A fractionally integrated approach," Journal of Economics and Business, Elsevier, vol. 56(6), pages 501-526.
    30. Heiko T. Burret & Lars P. Feld & Kö & Ekkehard A. hler, 2014. "Panel Cointegration Tests on the Fiscal Sustainability of German Laender," CESifo Working Paper Series 4929, CESifo.

  139. Marcellino, M., 1997. "Temporal Disaggregation, Missing Observations, Outliers, and Forecasting: A Unifying Non-Model Based Procedures," Economics Working Papers eco97/30, European University Institute.

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  140. Massimiliano Marcellino, "undated". "Further Results on MSFE Encompassing," Working Papers 143, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.

  141. Massimiliano Marcellino & Grayham E. Mizon, "undated". "Small system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Working Papers 188, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Eilev S. Jansen, 2004. "Modelling inflation in the Euro Area," Working Paper 2004/10, Norges Bank.
    2. Gil-Alana, Luis A., 2003. "A Univariate Analysis of Unemployment and Inflation in Italy: A Fractionally Integrated Approach," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(2), November.
    3. Melike Bildirici & Elçin Aykaç Alp, 2012. "Minimum wage is efficient wage in Turkish labor market: TAR–cointegration analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(4), pages 1261-1270, June.
    4. Jolejole-Foreman, Maria Christina & Mallory, Mindy L. & Baylis, Katherine R., 2013. "Impact of Wheat and Rice Export Ban on Indian Market Integration," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150595, Agricultural and Applied Economics Association.
    5. Barone, Guglielmo & Mocetti, Sauro, 2014. "Natural disasters, growth and institutions: A tale of two earthquakes," Journal of Urban Economics, Elsevier, vol. 84(C), pages 52-66.
    6. Rita Duarte, 2009. "The dynamic effects of shocks to wages and prices in the United States and the Euro Area," Working Papers w200915, Banco de Portugal, Economics and Research Department.
    7. Paolo Paruolo & Riccardo Girardi, 2010. "Wages and prices in Europe before and after the onset of the Monetary Union," Economics and Quantitative Methods qf1009, Department of Economics, University of Insubria.
    8. David F. Hendry & Massimiliano Marcellino & Chiara Monfardini, 2008. "Foreword," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 711-714, December.
    9. Sauro Mocetti & Guglielmo Barone, 2013. "Natural disasters, economic growth and corruption: a tale from two earthquakes," ERSA conference papers ersa13p726, European Regional Science Association.
    10. Mariam Camarero & Gaetano D’Adamo & Cecilio Tamarit, 2018. "Differences in wage determination in the Eurozone," Working Papers 1811, Department of Applied Economics II, Universidad de Valencia.
    11. Binotti, Annetta Maria & Ghiani, Enrico, 2008. "Changes in aggregate supply conditions in Italy: A small econometric model and its policy implications," Journal of Policy Modeling, Elsevier, vol. 30(6), pages 1017-1039.
    12. Kurita, Takamitsu, 2010. "Empirical modeling of Japan's markup and inflation, 1976-2000," Journal of Asian Economics, Elsevier, vol. 21(6), pages 552-563, December.
    13. Camarero, Mariam & D’Adamo, Gaetano & Tamarit, Cecilio, 2021. "Differences in wage determination in the Eurozone: A challenge to the resilience of the common currency," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 183-199.

  142. Eliana La Ferrara & Massimiliano Marcellino, "undated". "TFP, Costs, and Public Infrastructure: An Equivocal Relationship," Working Papers 176, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Guido Ascari & Valeria di Cosmo, 2005. "Determinants of total factor productivity in the italian Regions," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2005(2).
    2. Duarte Bom, P.R. & Ligthart, J.E., 2008. "How Productive is Public Capital? A Meta-Analysis," Other publications TiSEM e841076c-c1df-4617-a1bd-9, Tilburg University, School of Economics and Management.
    3. Balázs Égert & Tomasz Koźluk & Douglas Sutherland, 2009. "Infrastructure and Growth: Empirical Evidence," OECD Economics Department Working Papers 685, OECD Publishing.
    4. E. Marrocu & R. Paci, 2006. "The effects of public capital on the productivity of the Italian regions," Working Paper CRENoS 200613, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    5. Federico Bonaglia & Eliana La Ferrara & Massimiliano Marcellino, 2000. "Public Capital and Economic Performance: Evidence from Italy," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 59(2), pages 221-244, September.
    6. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2010. "Dynamic Macroeconomic Effects of Public Capital: Evidence from Regional Italian Data," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 69(1), pages 29-66, April.
    7. Pedro R.D. Bom & Jenny E. Ligthart, 2009. "How Productive is Public Capital? A Meta-Regression Analysis," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper0912, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    8. Francesco Aiello & Camilla Mastromarco & Angelo Zago, 2011. "Be productive or face decline. On the sources and determinants of output growth in Italian manufacturing firms," Empirical Economics, Springer, vol. 41(3), pages 787-815, December.
    9. Salvatore Amico Roxas & Antonio Cristofaro & Giuseppe Piroli, 2012. "Public Capital in the Private Sector of Italian Economy," EERI Research Paper Series EERI_RP_2012_19, Economics and Econometrics Research Institute (EERI), Brussels.
    10. Ward Romp & Jakob De Haan, 2007. "Public Capital and Economic Growth: A Critical Survey," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 8(S1), pages 6-52, April.
    11. Farhadi, Minoo, 2015. "Transport infrastructure and long-run economic growth in OECD countries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 73-90.
    12. Leonzio Rizzo & Riccardo Secomandi, 2018. "Lo stock di capitale comunale: indicazioni per le politiche di intervento infrastrutturale di Regione Lombardia," Working Papers 2018117, University of Ferrara, Department of Economics.
    13. Trofimov, Ivan D., 2020. "Public capital and productive economy profits: evidence from OECD economies," MPRA Paper 106848, University Library of Munich, Germany.
    14. Torrisi, Gianpiero, 2009. "Infrastructures and economic performance: a critical comparison across four approaches," MPRA Paper 18688, University Library of Munich, Germany.
    15. Davide Piacentino, 2008. "Productivity, Infrastructures and Convergence: Panel Data Evidence on Italian Regions," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2008(2), pages 5-26.
    16. Torrisi, Gianpiero, 2009. "A multilevel analysis on the economic impact of public infrastructure and corruption on Italian regions," MPRA Paper 15487, University Library of Munich, Germany.
    17. Víctor Adame & Javier Alonso & Luisa Pérez & David Tuesta, 2017. "Infrastructure & economic growth from a meta-analysis approach: do all roads lead to Rome?," Working Papers 17/07, BBVA Bank, Economic Research Department.
    18. R. Pala & E. Marrocu & R. Paci, 2000. "Estimation of total factor productivity for regions and sectors in Italy. A panel cointegration approach," Working Paper CRENoS 200016, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    19. Federici, Andrea, 2018. "Il rapporto tra capitale pubblico e altre variabili macroeconomiche: analisi della letteratura [The relationship between public capital and other macroeconomic variable: a literature review]," MPRA Paper 88515, University Library of Munich, Germany.
    20. Torrisi, Gianpiero, 2009. "Public infrastructure: definition, classification and measurement issues," MPRA Paper 12990, University Library of Munich, Germany.
    21. Daniel Montolio & Albert Solé‐Ollé, 2009. "Road investment and regional productivity growth: the effects of vehicle intensity and congestion," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 99-118, March.
    22. Juan A. Núñez-Serrano & Francisco J. Velázquez, 2017. "Is Public Capital Productive? Evidence from a Meta-analysis," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 39(2), pages 313-345.

  143. Federico Bonaglia & Eliana La Ferrara & Massimiliano Marcellino, "undated". "Public Capital and Economic Performance: Evidence from Italy," Working Papers 163, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Guido Ascari & Valeria di Cosmo, 2005. "Determinants of total factor productivity in the italian Regions," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2005(2).
    2. Raffaello Bronzini & Paolo Piselli, 2006. "Determinants of long-run regional productivity: the role of R&D, human capital and public infrastructure," Temi di discussione (Economic working papers) 597, Bank of Italy, Economic Research and International Relations Area.
    3. Duarte Bom, P.R. & Ligthart, J.E., 2008. "How Productive is Public Capital? A Meta-Analysis," Other publications TiSEM e841076c-c1df-4617-a1bd-9, Tilburg University, School of Economics and Management.
    4. Aiello, Francesco & Pupo, Valeria, 2012. "Structural funds and the economic divide in Italy," Journal of Policy Modeling, Elsevier, vol. 34(3), pages 403-418.
    5. Angel de la Fuente, 2015. "Infrastructures and Productivity: an Updated Survey," Working Papers 475, Barcelona School of Economics.
    6. E. Marrocu & R. Paci, 2006. "The effects of public capital on the productivity of the Italian regions," Working Paper CRENoS 200613, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    7. Bo Zhou & Yanping Xu & Seul Ki Lee, 2019. "Tourism development and regional production efficiency: Evidence from southwestern China," Tourism Economics, , vol. 25(5), pages 800-818, August.
    8. Raffaello Bronzini & Paolo Piselli, 2005. "What determines productivity level in the long run? Evidence from Italians regions," ERSA conference papers ersa05p267, European Regional Science Association.
    9. Anu Tokila & Mika Haapanen, 2012. "Evaluation of Deadweight Spending in Regional Enterprise Financing," Regional Studies, Taylor & Francis Journals, vol. 46(2), pages 185-201, May.
    10. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2010. "Dynamic Macroeconomic Effects of Public Capital: Evidence from Regional Italian Data," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 69(1), pages 29-66, April.
    11. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2012. "The Macroeconomic Impact of Infrastructures: A Literature Review and Empirical Analysis on the Case of Italy," QA - Rivista dell'Associazione Rossi-Doria, Associazione Rossi Doria, issue 1, March.
    12. Pedro R.D. Bom & Jenny E. Ligthart, 2009. "How Productive is Public Capital? A Meta-Regression Analysis," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper0912, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    13. Francesco Aiello & Camilla Mastromarco & Angelo Zago, 2011. "Be productive or face decline. On the sources and determinants of output growth in Italian manufacturing firms," Empirical Economics, Springer, vol. 41(3), pages 787-815, December.
    14. Kemmerling, Achim & Stephan, Andreas, 2015. "Comparative political economy of regional transport infrastructure investment in Europe," Journal of Comparative Economics, Elsevier, vol. 43(1), pages 227-239.
    15. Xueliang Zhang, 2008. "Transport infrastructure, spatial spillover and economic growth: Evidence from China," Psychometrika, Springer;The Psychometric Society, vol. 3(4), pages 585-597, December.
    16. Francesco Aiello & Alfonsina Iona & Leone Leonida, 2012. "Regional infrastructure and firm investment: theory and empirical evidence for Italy," Empirical Economics, Springer, vol. 42(3), pages 835-862, June.
    17. Barilla, David & Carlucci, Fabio & Cirà, Andrea & Ioppolo, Giuseppe & Siviero, Lucio, 2020. "Total factor logistics productivity: A spatial approach to the Italian regions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 205-222.
    18. Salvatore Amico Roxas & Antonio Cristofaro & Giuseppe Piroli, 2012. "Public Capital in the Private Sector of Italian Economy," EERI Research Paper Series EERI_RP_2012_19, Economics and Econometrics Research Institute (EERI), Brussels.
    19. Roberto Iacono, 2015. "No blessing, no curse? On the bene fits of being a resource-rich southern region of Italy," Working Papers - Economics wp2015_03.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    20. Paolo Pinotti, 2012. "The economic costs of organized crime: evidence from southern Italy," Temi di discussione (Economic working papers) 868, Bank of Italy, Economic Research and International Relations Area.
    21. Jordan Roulleau-Pasdeloup, 2013. "The Productive Government Spending Multiplier, In and Out of The Zero Lower Bound," Working Papers 2013-02, Center for Research in Economics and Statistics.
    22. Ward Romp & Jakob De Haan, 2007. "Public Capital and Economic Growth: A Critical Survey," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 8(S1), pages 6-52, April.
    23. Eliana La Ferrara & Massimiliano Marcellino, "undated". "TFP, Costs, and Public Infrastructure: An Equivocal Relationship," Working Papers 176, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    24. Gwenaelle Poilon & Jérôme Creel, 2008. "Is public capital productive in Europe?," SciencePo Working papers Main hal-03416671, HAL.
    25. Melo, Patricia C. & Graham, Daniel J. & Brage-Ardao, Ruben, 2013. "The productivity of transport infrastructure investment: A meta-analysis of empirical evidence," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 695-706.
    26. Jinrui Zhang & Ruilian Zhang & Junzhuo Xu & Jie Wang & Guoqing Shi, 2021. "Infrastructure Investment and Regional Economic Growth: Evidence from Yangtze River Economic Zone," Land, MDPI, vol. 10(3), pages 1-14, March.
    27. Valter Di Giacinto & Giorgio Nuzzo, 2006. "Explaining labour productivity differentials across Italian regions: the role of socio‐economic structure and factor endowments," Papers in Regional Science, Wiley Blackwell, vol. 85(2), pages 299-320, June.
    28. E. Marrocu & R. Paci & F. Pigliaru, 2006. "Gli effetti del capitale pubblico sulla produttività delle regioni italiane," Working Paper CRENoS 200601, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    29. Torrisi, Gianpiero, 2009. "Infrastructures and economic performance: a critical comparison across four approaches," MPRA Paper 18688, University Library of Munich, Germany.
    30. Joanna Mackiewicz-Łyziak, 2010. "Wpływ infrastruktury na produktywność w gospodarce Polski," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 41-61.
    31. Zhang, Liyunpeng & Zhuang, Yuhang & Ding, Yibing & Liu, Ziwei, 2023. "Infrastructure and poverty reduction: Assessing the dynamic impact of Chinese infrastructure investment in sub-Saharan Africa," Journal of Asian Economics, Elsevier, vol. 84(C).
    32. Li, Yan & Chen, Zhenhua & Wang, Peng, 2020. "Impact of high-speed rail on urban economic efficiency in China," Transport Policy, Elsevier, vol. 97(C), pages 220-231.
    33. Bronzini, Raffaello & Piselli, Paolo, 2009. "Determinants of long-run regional productivity with geographical spillovers: The role of R&D, human capital and public infrastructure," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 187-199, March.
    34. Papagni, Erasmo & Lepore, Amedeo & Felice, Emanuele & Baraldi, Anna Laura & Alfano, Maria Rosaria, 2021. "Public investment and growth: Lessons learned from 60-years experience in Southern Italy," Journal of Policy Modeling, Elsevier, vol. 43(2), pages 376-393.
    35. Torrisi, Gianpiero, 2009. "A multilevel analysis on the economic impact of public infrastructure and corruption on Italian regions," MPRA Paper 15487, University Library of Munich, Germany.
    36. Mao, Xia & Chen, Xiao, 2023. "Does airport construction narrow regional economic disparities in China?," Journal of Air Transport Management, Elsevier, vol. 108(C).
    37. Sergio Destefanis & Vania Sena, 2005. "Public capital and total factor productivity: New evidence from the Italian regions, 1970-98," Regional Studies, Taylor & Francis Journals, vol. 39(5), pages 603-617.
    38. Valter Di Giacinto & Giorgio Nuzzo, 2004. "Explaining labor productivity differentials on Italian regions," ERSA conference papers ersa04p105, European Regional Science Association.
    39. Xinhai Lu & Jiao Hou & Yifeng Tang & Ting Wang & Tianyi Li & Xupeng Zhang, 2022. "Evaluating the Impact of the Highway Infrastructure Construction and the Threshold Effect on Cultivated Land Use Efficiency: Evidence from Chinese Provincial Panel Data," Land, MDPI, vol. 11(7), pages 1-20, July.
    40. Víctor Adame & Javier Alonso & Luisa Pérez & David Tuesta, 2017. "Infrastructure & economic growth from a meta-analysis approach: do all roads lead to Rome?," Working Papers 17/07, BBVA Bank, Economic Research Department.
    41. R. Pala & E. Marrocu & R. Paci, 2000. "Estimation of total factor productivity for regions and sectors in Italy. A panel cointegration approach," Working Paper CRENoS 200016, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    42. Federici, Andrea, 2018. "Il rapporto tra capitale pubblico e altre variabili macroeconomiche: analisi della letteratura [The relationship between public capital and other macroeconomic variable: a literature review]," MPRA Paper 88515, University Library of Munich, Germany.
    43. Fedderke, Johannes W. & Bogetic & Zeljko, 2006. "Infrastructure and growth in South Africa : direct and indirect productivity impacts of 19 infrastructure measures," Policy Research Working Paper Series 3989, The World Bank.
    44. Beckman, Jayson & Hertel, Thomas, 2009. "Why Previous Estimates of the Cost of Climate Mitigation Might Be Too Low," Conference papers 331860, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    45. Erasmo Papagni & Amedeo Lepore & Emanuele Felice & Anna Laura Baraldi & Maria Rosaria Alfano, 2018. "Public Investment and Growth Accelerations: The Case of Southern Italy, 1951-1995," EERI Research Paper Series EERI RP 2018/10, Economics and Econometrics Research Institute (EERI), Brussels.
    46. Torrisi, Gianpiero, 2009. "Public infrastructure: definition, classification and measurement issues," MPRA Paper 12990, University Library of Munich, Germany.
    47. Holmgren, Johan & Merkel, Axel, 2017. "Much ado about nothing? – A meta-analysis of the relationship between infrastructure and economic growth," Research in Transportation Economics, Elsevier, vol. 63(C), pages 13-26.
    48. Silvia Bertarelli, 2006. "Public capital and growth," Politica economica, Società editrice il Mulino, issue 3, pages 361-398.
    49. Valeria Pupo & Francesco Aiello, 2009. "L'impatto della politica regionale dell'Unione Europea. Uno studio sulle regioni italiane," Rivista italiana degli economisti, Società editrice il Mulino, issue 3, pages 421-454.
    50. Sergio Destefanis & Vania Sena, 2009. "Public capital, productivity and trade balances: some evidence for the Italian regions," Empirical Economics, Springer, vol. 37(3), pages 533-554, December.

  144. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, "undated". "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Berner, Anne & Bruns, Stephan B. & Moneta, Alessio & Stern, David I., 2021. "Do energy efficiency improvements reduce energy use? Empirical evidence on the economy-wide rebound effect in Europe and the United States," University of Göttingen Working Papers in Economics 422, University of Goettingen, Department of Economics.
    2. Fabio C. Bagliano & Claudio Morana, 2006. "International Macroeconomic Dynamics: a Factor Vector Autoregressive Approach," ICER Working Papers 41-2006, ICER - International Centre for Economic Research.
    3. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    4. Farmer, Roger & Henry, Jerome & Marcellino, Massimiliano & Beyer, Andreas, 2005. "Factor Analysis in a New-Keynesian Model," CEPR Discussion Papers 5266, C.E.P.R. Discussion Papers.
    5. Matteo Manera & Massimiliano Serati & Michele Plotegher, 2008. "Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal," Working Papers 2008.9, Fondazione Eni Enrico Mattei.
    6. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    7. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    8. Bayar, Omer, 2018. "Weak instruments and estimated monetary policy rules," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 308-317.
    9. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    10. Jean Boivin & Marc P. Giannoni & Benoît Mojon, 2008. "How Has the Euro Changed the Monetary Transmission?," NBER Working Papers 14190, National Bureau of Economic Research, Inc.
    11. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2010. "Forecasting with Factor-augmented Error Correction Models," CEPR Discussion Papers 7677, C.E.P.R. Discussion Papers.
    12. Dimitris Korobilis, 2008. "Forecasting in vector autoregressions with many predictors," Advances in Econometrics, in: Bayesian Econometrics, pages 403-431, Emerald Group Publishing Limited.
    13. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    14. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    15. Massimiliano Marcellino & George Kapetanios, 2006. "The Role of Search Frictions and Bargaining for Inflation Dynamics," Working Papers 305, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    16. Eijffinger, S.C.W. & Mahieu, R.J. & Raes, L.B.D., 2010. "The Bond Yield Conundrum : Alternative Hypotheses and the State of the Economy," Discussion Paper 2010-121, Tilburg University, Center for Economic Research.
    17. Marc Hallin & Roman Liska, 2008. "Dynamic Factors in the Presence of Block Structure," Working Papers ECARES 2008_012, ULB -- Universite Libre de Bruxelles.
    18. Barigozzi, Matteo & Conti, Antonio & Luciani, Matteo, 2012. "Do Euro area countries respond asymmetrically to the common monetary policy?," LSE Research Online Documents on Economics 43344, London School of Economics and Political Science, LSE Library.
    19. Erdemlioglu, Deniz, 2009. "Macro Factors in UK Excess Bond Returns: Principal Components and Factor-Model Approach," MPRA Paper 28895, University Library of Munich, Germany.
    20. Hwee Kwan Chow & Keen Meng Choy, 2009. "Monetary Policy and Asset Prices in a Small Open Economy: A Factor-Augmented VAR Analysis for Singapore," Working Papers 11-2009, Singapore Management University, School of Economics.
    21. Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta, 2008. "Large Bayesian VARs," Working Paper Series 966, European Central Bank.
    22. Alonso Gomez & John M Maheu & Alex Maynard, 2008. "Improving Forecasts of Inflation using the Term Structure of Interest Rates," Working Papers tecipa-319, University of Toronto, Department of Economics.
    23. Fabio C. Bagliano & Claudio Morana, 2007. "Business Cycle Comovement in the G-7: Common Shocks or Common Transmission Mechanisms?," Carlo Alberto Notebooks 40, Collegio Carlo Alberto.
    24. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.
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    30. Sarafidis, Vasilis & Yamagata, Takashi, 2010. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors under Cross-sectional Dependence," MPRA Paper 25182, University Library of Munich, Germany.
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    32. Cui, Guowei & Sarafidis, Vasilis & Yamagata, Takashi, 2020. "IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude Toward Risk," MPRA Paper 102488, University Library of Munich, Germany.
    33. Hansen, Stephen & McMahon, Michael, 2015. "Shocking language: Understanding the macroeconomic effects of central bank communication," Economic Research Papers 269727, University of Warwick - Department of Economics.
    34. Harun Mirza & Lidia Storjohann, 2014. "Making Weak Instrument Sets Stronger: Factor‐Based Estimation of Inflation Dynamics and a Monetary Policy Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 643-664, June.
    35. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    36. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    37. António Rua & Francisco Craveiro Dias, 2008. "Determining the number of factors in approximate factor models with global and group-specific factors," Working Papers w200809, Banco de Portugal, Economics and Research Department.
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    40. He, Qing & Leung, Pak-Ho & Chong, Terence Tai-Leung, 2013. "Factor-augmented VAR analysis of the monetary policy in China," China Economic Review, Elsevier, vol. 25(C), pages 88-104.
    41. Sánchez-Fung, José R., 2011. "Estimating monetary policy reaction functions for emerging market economies: The case of Brazil," Economic Modelling, Elsevier, vol. 28(4), pages 1730-1738, July.
    42. Choi, In, 2012. "Efficient Estimation Of Factor Models," Econometric Theory, Cambridge University Press, vol. 28(2), pages 274-308, April.
    43. Mönch, Emanuel, 2005. "Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach," Working Paper Series 544, European Central Bank.
    44. In Choi, 2012. "Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons," Working Papers 1209, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    45. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
    46. Mirza, Harun & Storjohann, Lidia, 2011. "Making a Weak Instrument Set Stronger: Factor-Based Estimation of the Taylor Rule," Bonn Econ Discussion Papers 13/2011, University of Bonn, Bonn Graduate School of Economics (BGSE).
    47. Luccas Assis Attilio, 2024. "Transmission and impact of stock market shocks on the world economy," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 24(1).
    48. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas F., 2007. "Measuring Regional Market Integration in Developing Asia: a Dynamic Factor Error Correction Model (DF-ECM) Approach," Working Papers on Regional Economic Integration 8, Asian Development Bank.
    49. George Kapetanios & Massimiliano Marcellino, 2008. "Cross-sectional Averaging and Instrumental Variable Estimation with Many Weak Instruments," Working Papers 627, Queen Mary University of London, School of Economics and Finance.
    50. Porshakov, Alexey & Deryugina, Elena & Ponomarenko, Alexey & Sinyakov, Andrey, 2015. "Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model," BOFIT Discussion Papers 19/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    51. Ilse Botha, 2010. "A Comparative Analysis Of The Synchronisation Of Business Cycles For Developed And Developing Economies With The World Business Cycle," South African Journal of Economics, Economic Society of South Africa, vol. 78(2), pages 192-207, June.
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    53. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    54. Jean Boivin & Marc P. Giannoni & Benoît Mojon, 2009. "How Has the Euro Changed the Monetary Transmission Mechanism?," NBER Chapters, in: NBER Macroeconomics Annual 2008, Volume 23, pages 77-125, National Bureau of Economic Research, Inc.
    55. Gianluca Lagana, 2004. "Measuring monetary policy in the UK: a factor augmented vector autoregressive approach," Money Macro and Finance (MMF) Research Group Conference 2004 64, Money Macro and Finance Research Group.
    56. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.
    57. Pappa, Evi & Molteni, Francesco, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," CEPR Discussion Papers 12541, C.E.P.R. Discussion Papers.
    58. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 27-42, March.
    59. Marcellino, Massimiliano & Kapetanios, George, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers.
    60. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    61. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).
    62. Blaes, Barno, 2009. "Money and monetary policy transmission in the euro area: evidence from FAVAR- and VAR approaches," Discussion Paper Series 1: Economic Studies 2009,18, Deutsche Bundesbank.
    63. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2020. "IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude," Monash Econometrics and Business Statistics Working Papers 11/20, Monash University, Department of Econometrics and Business Statistics.
    64. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper series 53_12, Rimini Centre for Economic Analysis.
    65. Guenter Beck & Massimiliano Marcellino, 2006. "Regional Inflation Dynamics within and across Euro Area and a Comparison with the US," Computing in Economics and Finance 2006 338, Society for Computational Economics.
    66. George Kapetanios & Massimiliano Marcellino, 2006. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," Working Papers 577, Queen Mary University of London, School of Economics and Finance.
    67. Forni, Mario & Cavicchioli, Maddalena & Lippi, Marco & Zaffaroni, Paolo, 2016. "Eigenvalue Ratio Estimators for the Number of Common Factors," CEPR Discussion Papers 11440, C.E.P.R. Discussion Papers.
    68. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    69. Lu, Biao & Wu, Liuren, 2009. "Macroeconomic releases and the interest rate term structure," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 872-884, September.
    70. Festic, Mejra & Repina, Sebastijan & Volcjak, Robert, 2010. "Estimating Coal Price Dynamics with the Principal Components Method," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 188-212, July.
    71. Reichlin, Lucrezia & Giannone, Domenico & Banbura, Marta, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
    72. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," Working Papers ECARES ECARES 2011-019, ULB -- Universite Libre de Bruxelles.
    73. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas F. Quising, 2006. "Measuring Regional Market Integration by Dynamic Factor Error Correction Model (DF-ECM) Approach - The Case of Developing Asia," Working Papers 565, Queen Mary University of London, School of Economics and Finance.
    74. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    75. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2007. "Factor Analysis in a Model with Rational Expectations," NBER Working Papers 13404, National Bureau of Economic Research, Inc.
    76. Pallara, Kevin, 2016. "The dynamic effects of government spending: a FAVAR approach," MPRA Paper 92283, University Library of Munich, Germany.
    77. Giuliano Queiroz Ferreira & Leonardo Bornacki Mattos, 2022. "Regime-dependent price puzzle in the Brazilian economy: evidence from VAR and FAVAR approaches," SN Business & Economics, Springer, vol. 2(9), pages 1-28, September.
    78. Oyenyinka Sunday Omoshoro‐Jones & Lumengo Bonga‐Bonga, 2022. "Intra‐regional spillovers from Nigeria and South Africa to the rest of Africa: New evidence from a FAVAR model," The World Economy, Wiley Blackwell, vol. 45(1), pages 251-275, January.
    79. Krokida, Styliani-Iris & Makrychoriti, Panagiota & Spyrou, Spyros, 2020. "Monetary policy and herd behavior: International evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 386-417.
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    81. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
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    83. Fabio C. Bagliano & Claudio Morana, 2006. "A New Approach to Factor Vector Autoregressive Estimation with an Application to Large-Scale Macroeconometric Modelling," Carlo Alberto Notebooks 28, Collegio Carlo Alberto.
    84. Bodnar, Taras & Reiß, Markus, 2016. "Exact and asymptotic tests on a factor model in low and large dimensions with applications," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 125-151.
    85. Kakeu, Johnson & Bouaddi, Mohammed, 2017. "Empirical evidence of news about future prospects in the risk-pricing of oil assets," Energy Economics, Elsevier, vol. 64(C), pages 458-468.
    86. Andrea Beltratti & Claudio Morana, 2008. "International shocks and national house prices," ICER Working Papers - Applied Mathematics Series 14-2008, ICER - International Centre for Economic Research.
    87. Yunus Emre Ergemen, 2022. "Parametric Estimation of Long Memory in Factor Models," CREATES Research Papers 2022-10, Department of Economics and Business Economics, Aarhus University.
    88. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.
    89. Agostino Consolo, 2006. "Forecasting measures of inflation for the Estonian economy," Bank of Estonia Working Papers 2006-03, Bank of Estonia, revised 12 Nov 2006.
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    92. Dobrescu, Emilian & Gaftea, Viorel & Scutaru, Cornelia, 2010. "Using the Leontief Matrix to Estimate the Impact of Investments upon the Global Output," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 176-187, July.
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  145. Giampiero M. Gallo & Massimiliano Marcellino, "undated". "Ex Post and Ex Ante Analysis of Provisional Data," Working Papers 141, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

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    1. Andres Fernandez & Norman R. Swanson, 2009. "Real-time datasets really do make a difference: definitional change, data release, and forecasting," Working Papers 09-28, Federal Reserve Bank of Philadelphia.
    2. Fabio Busetti, 2006. "Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 1-23.
    3. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.

  146. Massimiliano Marcellino & James H. Stock & Mark W. Watson, "undated". "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
    2. Libero Monteforte, 2004. "Aggregation bias in macro models: does it matter foir the euro area?," Temi di discussione (Economic working papers) 534, Bank of Italy, Economic Research and International Relations Area.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. Michael Graff, 2005. "Internationale Konjunkturverbunde," KOF Working papers 05-108, KOF Swiss Economic Institute, ETH Zurich.
    5. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    6. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
    7. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, May.
    8. Guilherme Schultz Lindenmeyer & Hudson Silva Torrent, 2024. "Boosting and Predictability of Macroeconomic Variables: Evidence from Brazil," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 377-409, July.
    9. Vivek Ghosal & Yang Ye, 2015. "Uncertainty and the employment dynamics of small and large businesses," Small Business Economics, Springer, vol. 44(3), pages 529-558, March.
    10. Heather M. Anderson & Mardi Dungey & Denise R Osborn & Farshid Vahid, 2010. "Financial Integration and the Construction of Historical Financial Data for the Euro Area," Centre for Growth and Business Cycle Research Discussion Paper Series 152, Economics, The University of Manchester.
    11. Giovanni Di Bartolomeo & Francesco Giuli & Marco manzo, 2005. "Policy Uncertainty, Symbiosis, and the Optimal Fiscal and Monetary Conservativeness," Macroeconomics 0508005, University Library of Munich, Germany.
    12. Troy Matheson, 2005. "Factor model forecasts for New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2005/01, Reserve Bank of New Zealand.
    13. Mario Forno & Marco Lippi & Lucrezia Reichlin & Filippo Altissimo & Antonio Bassanetti, 2003. "Eurocoin: A Real Time Coincident Indicator Of The Euro Area Business Cycle," Computing in Economics and Finance 2003 242, Society for Computational Economics.
    14. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    15. Matteo Manera & Massimiliano Serati & Michele Plotegher, 2008. "Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal," Working Papers 2008.9, Fondazione Eni Enrico Mattei.
    16. Colin Bermingham & Antonello D’Agostino, 2014. "Understanding and forecasting aggregate and disaggregate price dynamics," Empirical Economics, Springer, vol. 46(2), pages 765-788, March.
    17. António Rua & Luís Catela Nunes, 2003. "Coincident and Leading Indicators for the Euro Area: A Frequency Band Approach," Working Papers w200307, Banco de Portugal, Economics and Research Department.
    18. Kelly Burns, 2016. "A Reconsideration of the Meese-Rogoff Puzzle: An Alternative Approach to Model Estimation and Forecast Evaluation," Multinational Finance Journal, Multinational Finance Journal, vol. 20(1), pages 41-83, March.
    19. António Rua & Carlos Melo Gouveia & Nuno Lourenço, 2020. "Forecasting tourism with targeted predictors in a data-rich environment," Working Papers w202005, Banco de Portugal, Economics and Research Department.
    20. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    21. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate Pass-Through in the Major European Economies - The Role of Expectations," Discussion Papers 10/03, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    22. Kilian, Lutz & Inoue, Atsushi, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.
    23. WAN, Shui-Ki & WANG, Shin-Huei & WOO, Chi-Keung, 2012. "Total tourist arrival forecast: aggregation vs. disaggregation," LIDAM Discussion Papers CORE 2012039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    24. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
    25. Chien-jung Ting & Yi-Long Hsiao & Rui-jun Su, 2022. "Application of the Real-Time Tourism Data in Nowcasting the Service Consumption in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(4), pages 1-4.
    26. Luci Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon M. Potter, 2014. "Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences," Staff Reports 680, Federal Reserve Bank of New York.
    27. Giovanni Di Bartolomeo & Marco Manzo, 2010. "Fiscal Policy Under Balanced Budget And Indeterminacy: A New Keynesian Perspective," Scottish Journal of Political Economy, Scottish Economic Society, vol. 57(4), pages 455-472, September.
    28. Wolters, Maik Hendrik, 2012. "Evaluating point and density forecasts of DSGE models," MPRA Paper 36147, University Library of Munich, Germany.
    29. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    30. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    31. Francesco Ravazzolo & Shaun P. Vahey, 2010. "Forecast densities for economic aggregates from disaggregate ensembles," Working Paper 2010/02, Norges Bank.
    32. Emma SARNO & Alberto ZAZZARO, 2003. "Structural Convergence of Macroeconomic Time Series: Evidence for Inflation Rates in EU Countries," Working Papers 180, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    33. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    34. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 214, European Central Bank.
    35. Katja Drechsel & Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
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    41. Angelos Kanas, 2008. "Modeling regime transition in stock index futures markets and forecasting implications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 649-669.
    42. Maximo Camacho, 2002. "Nonlinear stochastic trends and economic fluctuations," Computing in Economics and Finance 2002 274, Society for Computational Economics.
    43. Kausik Chaudhuri & Alok Kumar, 2015. "A Markov-Switching Model for Indian Stock Price and Volume," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 14(3), pages 239-257, December.
    44. Declerck , Francis & Indjehagopian , Jean-Pierre & Bellocq , Flavien, 2015. "Relation entre le prix du pétrole et les cours boursiers des grandes compagnies pétrolières mondiales," ESSEC Working Papers WP1504, ESSEC Research Center, ESSEC Business School.
    45. Isaac Abunyuwah & Henry De-Graft Acquah, 2013. "Modelling non-linear Spatial Market Integration and Equilibrium Processes in Hidden Markov Framework," Journal of Economics and Behavioral Studies, AMH International, vol. 5(8), pages 535-545.
    46. Philip Kostov & John Lingard, 2004. "Regime-switching Vector Error Correction Model (VECM) analysis of UK meat consumption," Econometrics 0409007, University Library of Munich, Germany.
    47. Dennis Kristensen & Anders Rahbek, 2007. "Likelihood-Based Inference in Nonlinear Error-Correction Models," CREATES Research Papers 2007-38, Department of Economics and Business Economics, Aarhus University.
    48. Matteo Manera & Alessandro Cologni, 2006. "The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis for the G-7 Countries," Working Papers 2006.29, Fondazione Eni Enrico Mattei.
    49. Camacho, Maximo, 2005. "Markov-switching stochastic trends and economic fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 135-158, January.
    50. de Morais, Igor Alexandre C. & Portugal, Marcelo Savino, 2005. "A Markov Switching Model for the Brazilian Demand for Imports: Analyzing the Import Substitution Process in Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(2), November.
    51. Evangelia Papapetrou, 2013. "Oil prices and economic activity in Greece," Economic Change and Restructuring, Springer, vol. 46(4), pages 385-397, November.
    52. Brümmer, Bernhard & von Cramon-Taubadel, Stephan & Zorya, Sergiy, 2006. "Vertical Price Transmission between Wheat and Flour in Ukraine: A Markov-Switching Vector Error Correction Approach," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25575, International Association of Agricultural Economists.
    53. Marcelo Savino Portugal & Igor Alexandre Clemente de Morais, 2004. "Business Cycle In The Industrial Production Of Brazilian States," Econometric Society 2004 Latin American Meetings 23, Econometric Society.
    54. Vollmer, Teresa & von Cramon-Taubadel, Stephan, 2019. "The influence of Brazilian exports on price transmission processes in the coffee sector: A Markov-switching approach," DARE Discussion Papers 1904, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
    55. Jamel JOUINI, 2018. "Measuring the Macroeconomic Impacts of Fiscal Policy Shocks in the Saudi Economy : A Markov Switching Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 55-70, December.
    56. Ihle, Rico & von Cramon-Taubadel, Stephan, 2008. "A Comparison of Threshold Cointegration and Markov-Switching Vector Error Correction Models in Price Transmission Analysis," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37603, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    57. Carlo Di Giorgio, 2016. "Business Cycle Synchronization of CEECs with the Euro Area: A Regime Switching Approach," Journal of Common Market Studies, Wiley Blackwell, vol. 54(2), pages 284-300, March.
    58. Götz, Linde & Glauben, Thomas & Brümmer, Bernhard, 2013. "Wheat export restrictions and domestic market effects in Russia and Ukraine during the food crisis," Food Policy, Elsevier, vol. 38(C), pages 214-226.
    59. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    60. Tamayo Castaño, Jorge Andrés, 2012. "Asimetrías en la demanda por trabajo en Colombia : el papel del ciclo económico," Chapters, in: Arango-Thomas, Luis Eduardo & Hamann-Salcedo, Franz Alonso (ed.), El mercado de trabajo en Colombia : hechos, tendencias e instituciones, chapter 12, pages 487-542, Banco de la Republica de Colombia.
    61. Ricardo Troncoso-Sepúlveda, 2019. "Price transmission of rice in Colombia and the world," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 91, pages 151-179, Julio - D.
    62. Li, Leon, 2022. "The dynamic interrelations of oil-equity implied volatility indexes under low and high volatility-of-volatility risk," Energy Economics, Elsevier, vol. 105(C).
    63. Rezitis, A.N. & Ahammad, S.M., 2015. "Investigating Agricultural Production Relations across Bangladesh, India and Pakistan Using Vector Error Correction and Markov-Switching Models," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 28(01).
    64. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    65. Brümmer, Bernhard & Zorya, Sergiy, 2005. "Wheat / Flour Price Transmission and Agricultural Policies in Ukraine: A Markov-Switching Vector Error Correction Approach," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24624, European Association of Agricultural Economists.
    66. Ihle, Rico & von Cramon-Taubadel, Stephan, 2008. "Nonlinear Vector Error Correction Models in Price Transmission Analysis: Threshold Models vs. Markov-Switching Models," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44198, European Association of Agricultural Economists.
    67. Manera, Matteo & Cologni, Alessandro, 2006. "The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis," International Energy Markets Working Papers 12121, Fondazione Eni Enrico Mattei (FEEM).

  148. Massimiliano Marcellino & Grayham E. Mizon, "undated". "Modelling shifts in the wage-price and unemployment-inflation relationships in Italy, Poland, and the UK," Working Papers 145, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Adriatik Hoxha, 2016. "The Wage-Price Setting Behavior: Comparing The Evidence from EU28 and EMU," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 19(60), pages 61-102, June.
    2. Robalo Marques, Carlos, 2008. "Wage and price dynamics in Portugal," Working Paper Series 945, European Central Bank.
    3. Ana María Iregui & Jesús Otero, 2002. "On The Dynamics Of Unemployment In A Developing Economy: Colombia," Borradores de Economia 3298, Banco de la Republica.
    4. Jolejole-Foreman, Maria Christina & Mallory, Mindy L. & Baylis, Katherine R., 2013. "Impact of Wheat and Rice Export Ban on Indian Market Integration," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150595, Agricultural and Applied Economics Association.
    5. Rita Duarte, 2009. "The dynamic effects of shocks to wages and prices in the United States and the Euro Area," Working Papers w200915, Banco de Portugal, Economics and Research Department.
    6. Silvia Fedeli & Francesco Forte, 2009. "The Laffer effects of a program of deregulation cum detaxation: the Italian reform of labour contracts in the period 1997–2001," European Journal of Law and Economics, Springer, vol. 27(3), pages 211-232, June.
    7. David F. Hendry & Massimiliano Marcellino & Chiara Monfardini, 2008. "Foreword," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 711-714, December.
    8. Binotti, Annetta Maria & Ghiani, Enrico, 2008. "Changes in aggregate supply conditions in Italy: A small econometric model and its policy implications," Journal of Policy Modeling, Elsevier, vol. 30(6), pages 1017-1039.
    9. Ian Babetskii, 2007. "Aggregate Wage Flexibility in Selected New EU Member States," CESifo Working Paper Series 1916, CESifo.
    10. Massimiliano Marcellino & Grayham E. Mizon & Hans-Martin Krolzig, 2002. "A Markov-switching vector equilibrium correction model of the UK labour market," Empirical Economics, Springer, vol. 27(2), pages 233-254.
    11. Adriatik Hoxha, 2016. "The Switch to Near-Rational Wage-Price Setting Behaviour: The Case of United Kingdom," EuroEconomica, Danubius University of Galati, issue 1(35), pages 127-148, may.

  149. Massimiliano Marcellino, "undated". "Model Selection for Non-Linear Dynamic Models," Working Papers 159, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Kenneth Kasa & In-Koo Cho, 2011. "Learning and Model Validation," 2011 Meeting Papers 1086, Society for Economic Dynamics.

Articles

  1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2025. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(1), pages 57-73, January.
    See citations under working paper version above.
  2. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2025. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 27-43, January.
    See citations under working paper version above.
  3. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2024. "Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1302-1317, October.
    See citations under working paper version above.
  4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2024. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," The Review of Economics and Statistics, MIT Press, vol. 106(5), pages 1403-1417, September.
    See citations under working paper version above.
  5. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2024. "Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1099-1127, August.
    See citations under working paper version above.
  6. Carriero, Andrea & Marcellino, Massimiliano & Tornese, Tommaso, 2024. "Blended identification in structural VARs," Journal of Monetary Economics, Elsevier, vol. 146(C).
    See citations under working paper version above.
  7. Carriero, Andrea & Marcellino, Massimiliano & Tornese, Tommaso, 2023. "Macro uncertainty in the long run," Economics Letters, Elsevier, vol. 225(C).
    See citations under working paper version above.
  8. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    See citations under working paper version above.
  9. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Nowcasting tail risk to economic activity at a weekly frequency," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 843-866, August.
    See citations under working paper version above.
  10. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    See citations under working paper version above.
  11. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    See citations under working paper version above.
  12. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1230-1255, September.
    See citations under working paper version above.
  13. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2021. "No‐arbitrage priors, drifting volatilities, and the term structure of interest rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 495-516, August.
    See citations under working paper version above.
  14. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    See citations under working paper version above.
  15. Marcellino, Massimiliano & Sivec, Vasja, 2021. "Nowcasting Gdp Growth In A Small Open Economy," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 127-161, May.

    Cited by:

    1. Bolivar, Osmar, 2024. "GDP nowcasting: A machine learning and remote sensing data-based approach for Bolivia," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(3).
    2. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    3. Christian Glocker & Serguei Kaniovski, 2020. "Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models," WIFO Working Papers 614, WIFO.
    4. Bae, Juhee, 2024. "Factor-augmented forecasting in big data," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1660-1688.
    5. Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023. "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 213-234, March.
    6. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    7. Karlsson, Sune & Mazur, Stepan & Raftab, Mariya, 2025. "Identifying Useful Indicators for Nowcasting GDP in Sweden," Working Papers 2025:4, Örebro University, School of Business.

  16. Giraitis, Liudas & Kapetanios, George & Marcellino, Massimiliano, 2021. "Time-varying instrumental variable estimation," Journal of Econometrics, Elsevier, vol. 224(2), pages 394-415.
    See citations under working paper version above.
  17. Goulet Coulombe, Philippe & Marcellino, Massimiliano & Stevanović, Dalibor, 2021. "Can Machine Learning Catch The Covid-19 Recession?," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 71-109, May.
    See citations under working paper version above.
  18. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Assessing international commonality in macroeconomic uncertainty and its effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 273-293, April.
    See citations under working paper version above.
  19. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2020. "Markov-Switching Three-Pass Regression Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 285-302, April.
    See citations under working paper version above.
  20. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    See citations under working paper version above.
  21. Christian Hepenstrick & Massimiliano Marcellino, 2019. "Forecasting gross domestic product growth with large unbalanced data sets: the mixed frequency three‐pass regression filter," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(1), pages 69-99, January.

    Cited by:

    1. Chatelais, Nicolas & Stalla-Bourdillon, Arthur & Chinn, Menzie D., 2023. "Forecasting real activity using cross-sectoral stock market information," Journal of International Money and Finance, Elsevier, vol. 131(C).
    2. Nicolas Chatelais & Arthur Stalla-Bourdillon & Menzie D. Chinn, 2022. "Macroeconomic Forecasting using Filtered Signals from a Stock Market Cross Section," NBER Working Papers 30305, National Bureau of Economic Research, Inc.
    3. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
    4. Bae, Juhee, 2024. "Factor-augmented forecasting in big data," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1660-1688.
    5. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    6. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

  22. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    See citations under working paper version above.
  23. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.

    Cited by:

    1. Ha, Jongrim & Kose, M. Ayhan & Ohnsorge, Franziska, 2021. "Inflation During the Pandemic: What Happened? What is Next?," MPRA Paper 108677, University Library of Munich, Germany.
    2. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    3. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    4. Leonardo Nogueira Ferreira & Silvia Miranda-Agrippino & Giovanni Ricco, 2023. "Bayesian Local Projections," Working Papers Series 581, Central Bank of Brazil, Research Department.
    5. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    6. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting," Papers 2404.02671, arXiv.org, revised Nov 2024.
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    8. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    9. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    10. Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2024. "Labour at risk," European Economic Review, Elsevier, vol. 170(C).
    11. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    12. Annalisa Cadonna & Sylvia Fruhwirth-Schnatter & Peter Knaus, 2019. "Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models," Papers 1912.03100, arXiv.org.
    13. Juan Antolin-Diaz & Ivan Petrella & Juan F. Rubio-Ramirez, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," FRB Atlanta Working Paper 2021-25, Federal Reserve Bank of Atlanta.
    14. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    15. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    16. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    17. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea & Mertens, Elmar, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," CEPR Discussion Papers 15964, C.E.P.R. Discussion Papers.
    18. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2022. "APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1625-1658, November.
    19. Niko Hauzenberger & Florian Huber & Luca Onorante, 2021. "Combining shrinkage and sparsity in conjugate vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 304-327, April.
    20. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, vol. 9(2), pages 1-35, May.
    21. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    22. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    23. Tsionas, Mike, 2022. "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries," International Journal of Production Economics, Elsevier, vol. 249(C).
    24. Florian Huber & Gary Koop, 2024. "Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1301-1320, November.
    25. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2021. "Forecasting natural gas prices using highly flexible time-varying parameter models," Economic Modelling, Elsevier, vol. 105(C).
    26. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    27. Hauzenberger, Niko & Pfarrhofer, Michael & Rossini, Luca, 2025. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," International Journal of Forecasting, Elsevier, vol. 41(1), pages 361-376.
    28. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    29. Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023. "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, vol. 232(1), pages 52-69.
    30. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    31. Hauzenberger Niko & Huber Florian & Koop Gary, 2024. "Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 201-225, April.
    32. Frank C. Z. Wu, 2024. "Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(4), pages 697-704, June.
    33. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    34. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    35. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    36. Korobilis, Dimitris, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, vol. 148(C).
    37. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2022. "Reconciled Estimates of Monthly GDP in the US," Working Papers 22-01, Federal Reserve Bank of Cleveland.
    38. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    39. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    40. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    41. Javier Sánchez García & Salvador Cruz Rambaud, 2022. "Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    42. Edward S. Knotek & Saeed Zaman, 2020. "Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach," Working Papers 20-31, Federal Reserve Bank of Cleveland.
    43. Chen, Zhengyang & Valcarcel, Victor J., 2021. "Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    44. Sebastian Ankargren & Paulina Jon'eus, 2019. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Papers 1907.01075, arXiv.org.
    45. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    46. Florian Huber & Gary Koop & Massimiliano Marcellino & Tobias Scheckel, 2024. "Bayesian modelling of VAR precision matrices using stochastic block networks," Papers 2407.16349, arXiv.org.
    47. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    48. Ha, Jongrim & Kose, M. Ayhan & Ohnsorge, Franziska, 2022. "Global Stagflation," CEPR Discussion Papers 17381, C.E.P.R. Discussion Papers.
    49. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.
    50. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
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    67. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    68. Helmut Lütkepohl & Fei Shang & Luis Uzeda & Tomasz Woźniak, 2024. "Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference," Discussion Papers of DIW Berlin 2081, DIW Berlin, German Institute for Economic Research.
    69. Luis Gruber & Gregor Kastner, 2022. "Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!," Papers 2206.04902, arXiv.org, revised Feb 2025.
    70. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
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    73. Bognanni, Mark, 2022. "Comment on “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors”," Journal of Econometrics, Elsevier, vol. 227(2), pages 498-505.
    74. Ikhlaas Gurrib & Firuz Kamalov & Osama Atayah & Dalia Hemdan & Olga Starkova, 2024. "Long-Run Trade Relationship between the U.S. and Canada: The Case of the Canadian Dollar with the U.S. Dollar," JRFM, MDPI, vol. 17(9), pages 1-21, September.
    75. Antonio Pacifico, 2022. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure," Econometrics, MDPI, vol. 10(3), pages 1-24, July.
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    78. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.
    79. Michael Pfarrhofer & Anna Stelzer, 2019. "High-frequency and heteroskedasticity identification in multicountry models: Revisiting spillovers of monetary shocks," Papers 1912.03158, arXiv.org, revised Dec 2024.
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    81. Zhang, Wen, 2022. "China’s government spending and global inflation dynamics: The role of the oil price channel," Energy Economics, Elsevier, vol. 110(C).
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    83. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
    84. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2021. "General Bayesian time-varying parameter VARs for predicting government bond yields," Papers 2102.13393, arXiv.org.
    85. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    86. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    87. Tomasz Wo'zniak, 2024. "Fast and Efficient Bayesian Analysis of Structural Vector Autoregressions Using the R Package bsvars," Papers 2410.15090, arXiv.org.
    88. Dimitris Korobilis, 2020. "Sign restrictions in high-dimensional vector autoregressions," Working Paper series 20-09, Rimini Centre for Economic Analysis.
    89. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
    90. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2023. "Shadow-rate VARs," Discussion Papers 14/2023, Deutsche Bundesbank.
    91. Wu, Ping & Koop, Gary, 2023. "Estimating the ordering of variables in a VAR using a Plackett–Luce prior," Economics Letters, Elsevier, vol. 230(C).
    92. Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2023. "Labour at risk," Working Paper Series 2840, European Central Bank.
    93. Todd E. Clark & Matthew V. Gordon & Saeed Zaman, 2023. "Forecasting Core Inflation and Its Goods, Housing, and Supercore Components," Working Papers 23-34, Federal Reserve Bank of Cleveland.
    94. Cross, Jamie L. & Hou, Chenghan & Poon, Aubrey, 2020. "Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity," International Journal of Forecasting, Elsevier, vol. 36(3), pages 899-915.
    95. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    96. Andrea Carriero & Davide Pettenuzzo & Shubhranshu Shekhar, 2024. "Macroeconomic Forecasting with Large Language Models," Papers 2407.00890, arXiv.org, revised Mar 2025.
    97. Panovska, Irina & Zhang, Licheng, 2024. "Jobless recoveries and time variation in labor markets," Journal of Macroeconomics, Elsevier, vol. 81(C).
    98. Fischer, Manfred M. & Hauzenberger, Niko & Huber, Florian & Pfarrhofer, Michael, 2022. "General Bayesian time-varying parameter VARs for modeling government bond yields," Working Papers in Regional Science 2021/01, WU Vienna University of Economics and Business.
    99. Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021. "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series 2543, European Central Bank.
    100. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.
    101. Kunovac, Davor & Palenzuela, Diego Rodriguez & Sun, Yiqiao, 2022. "A new optimum currency area index for the euro area," Working Paper Series 2730, European Central Bank.
    102. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    103. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    104. Anna Pajor & Justyna Wróblewska & Łukasz Kwiatkowski & Jacek Osiewalski, 2024. "Hybrid SV‐GARCH, t‐GARCH and Markov‐switching covariance structures in VEC models—Which is better from a predictive perspective?," International Statistical Review, International Statistical Institute, vol. 92(1), pages 62-86, April.
    105. Kumar, Utkarsh & Ahmad, Wasim, 2024. "Navigating the “twin titans” of global manufacturing: The impact of US and China on industrial production forecasting in G20 nations," Pacific-Basin Finance Journal, Elsevier, vol. 87(C).
    106. Lukas Berend & Jan Pruser, 2024. "The Transmission of Monetary Policy via Common Cycles in the Euro Area," Papers 2410.05741, arXiv.org, revised Nov 2024.
    107. Martin Feldkircher & Luis Gruber & Florian Huber & Gregor Kastner, 2024. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2126-2145, September.
    108. Florian Eckert & Nina Mühlebach, 2023. "Global and local components of output gaps," Empirical Economics, Springer, vol. 65(5), pages 2301-2331, November.
    109. Zens, Gregor & Böck, Maximilian & Zörner, Thomas O., 2020. "The heterogeneous impact of monetary policy on the US labor market," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    110. Zhao, Jing, 2023. "Time-varying impact of geopolitical risk on natural resources prices: Evidence from the hybrid TVP-VAR model with large system," Resources Policy, Elsevier, vol. 82(C).
    111. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  24. Claudia Foroni & Massimiliano Marcellino & Dalibor Stevanovic, 2019. "Mixed‐frequency models with moving‐average components," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 688-706, August.

    Cited by:

    1. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    2. Luca Fanelli & Antonio Marsi, 2021. "Unconventional Monetary Policy in the Euro Area: A Tale of Three Shocks," Working Papers wp1164, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Fanelli, Luca & Marsi, Antonio, 2022. "Sovereign spreads and unconventional monetary policy in the Euro area: A tale of three shocks," European Economic Review, Elsevier, vol. 150(C).

  25. Fabio Bertolotti & Massimiliano Marcellino, 2019. "Tax shocks with high and low uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 972-993, September.
    See citations under working paper version above.
  26. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    See citations under working paper version above.
  27. Angela Abbate & Massimiliano Marcellino, 2018. "Point, interval and density forecasts of exchange rates with time varying parameter models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 155-179, January.
    See citations under working paper version above.
  28. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Measuring Uncertainty and Its Impact on the Economy," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 799-815, December.
    See citations under working paper version above.
  29. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2017. "Forecasting economic activity by Bayesian bridge model averaging," Empirical Economics, Springer, vol. 53(1), pages 21-40, August.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Donato Ceci & Andrea Silvestrini, 2023. "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.
    3. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 90110, University Library of Munich, Germany, revised 16 Nov 2018.
    4. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    5. José A. Tenreiro Machado & Maria Eugénia Mata & António M. Lopes, 2020. "Fractional Dynamics and Pseudo-Phase Space of Country Economic Processes," Mathematics, MDPI, vol. 8(1), pages 1-17, January.
    6. Stolbov, Mikhail & Shchepeleva, Maria, 2020. "What predicts the legal status of cryptocurrencies?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 273-291.

  30. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    See citations under working paper version above.
  31. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2017. "Explaining the time-varying effects of oil market shocks on US stock returns," Economics Letters, Elsevier, vol. 155(C), pages 84-88.
    See citations under working paper version above.
  32. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2017. "Structural FECM: Cointegration in large‐scale structural FAVAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1069-1086, September.
    See citations under working paper version above.
  33. Valentina Aprigliano & Claudia Foroni & Massimiliano Marcellino & Gianluigi Mazzi & Fabrizio Venditti, 2017. "A daily indicator of economic growth for the euro area," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 43-63.

    Cited by:

    1. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    2. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    3. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    4. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    5. Karlsson, Sune & Mazur, Stepan & Raftab, Mariya, 2025. "Identifying Useful Indicators for Nowcasting GDP in Sweden," Working Papers 2025:4, Örebro University, School of Business.

  34. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    See citations under working paper version above.
  35. Guenter W. Beck & Kirstin Hubrich & Massimiliano Marcellino, 2016. "On the Importance of Sectoral and Regional Shocks for Price‐Setting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1234-1253, November.
    See citations under working paper version above.
  36. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    See citations under working paper version above.
  37. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.

    Cited by:

    1. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    2. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    4. Goran Maksimović & Srđan Jović & David Jovović & Marina Jovović, 2019. "RETRACTED ARTICLE: Analyses of Economic Development Based on Different Factors," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1103-1109, March.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    6. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    7. Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.
    8. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    9. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    10. Marcel Ausloos & Roy Cerqueti & Francesca Bartolacci & Nicola G. Castellano, 2018. "SME investment best strategies. Outliers for assessing how to optimize performance," Papers 1807.09583, arXiv.org.
    11. Jović, Srđan & Maksimović, Goran & Jovović, David, 2016. "Appraisal of natural resources rents and economic development," Resources Policy, Elsevier, vol. 50(C), pages 289-291.
    12. Đokić, Aleksandar & Jović, Srđan, 2017. "Evaluation of agriculture and industry effect on economic health by ANFIS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 396-399.
    13. Maksimović, Goran & Jović, Srđan & Jovanović, Radomir, 2017. "Economic growth rate management by soft computing approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 520-524.
    14. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    15. Petri Kuosmanen & Juuso Vataja, 2017. "The return of financial variables in forecasting GDP growth in the G-7," Economic Change and Restructuring, Springer, vol. 50(3), pages 259-277, August.
    16. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    17. Oscar Claveria & Enric Monte & Salvador Torra, 2021. ""Nowcasting and forecasting GDP growth with machine-learning sentiment indicators"," IREA Working Papers 202103, University of Barcelona, Research Institute of Applied Economics, revised Feb 2021.

  38. Georges Kapetanios & Lynda Khalaf & Massimiliano Marcellino, 2016. "Factor‐Based Identification‐Robust Interference in IV Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(5), pages 821-842, August.

    Cited by:

    1. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.

  39. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    See citations under working paper version above.
  40. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    See citations under working paper version above.
  41. Claudia Foroni & Massimiliano Marcellino, 2016. "Mixed frequency structural vector auto-regressive models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 403-425, February.

    Cited by:

    1. Chudik, Alexander & Georgiadis, Georgios, 2019. "Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables," Working Paper Series 2307, European Central Bank.
    2. Davtyan, Karen, 2023. "Unconventional monetary policy and economic inequality," Economic Modelling, Elsevier, vol. 126(C).
    3. Haroon Mumtaz & Laura Sunder-Plassmann, 2017. "Non-linear effects of government spending shocks in the US. Evidence from state-level data," Working Papers 841, Queen Mary University of London, School of Economics and Finance.
    4. Stephen Snudden, 2024. "Don’t Ruin the Surprise: Temporal Aggregation Bias in Structural Innovations," LCERPA Working Papers jc0149, Laurier Centre for Economic Research and Policy Analysis, revised Nov 2024.
    5. Camacho, Maximo & Perez-Quiros, Gabriel & Pacce, Matías, 2020. "Spillover effects in international business cycles," Working Paper Series 2484, European Central Bank.
    6. Xin Sheng & Rangan Gupta, 2022. "The State-Level Nonlinear Effects of Government Spending Shocks in the US: The Role of Partisan Conflict," Sustainability, MDPI, vol. 14(18), pages 1-9, September.
    7. Agostino Consolo & Claudia Foroni & Catalina Martínez Hernández, 2023. "A Mixed Frequency BVAR for the Euro Area Labour Market," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 1048-1082, October.
    8. Luca Fanelli & Antonio Marsi, 2021. "Unconventional Monetary Policy in the Euro Area: A Tale of Three Shocks," Working Papers wp1164, Dipartimento Scienze Economiche, Universita' di Bologna.
    9. Fanelli, Luca & Marsi, Antonio, 2022. "Sovereign spreads and unconventional monetary policy in the Euro area: A tale of three shocks," European Economic Review, Elsevier, vol. 150(C).
    10. Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.

  42. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    See citations under working paper version above.
  43. Esteban Prieto & Sandra Eickmeier & Massimiliano Marcellino, 2016. "Time Variation in Macro‐Financial Linkages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1215-1233, November.
    See citations under working paper version above.
  44. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.

    Cited by:

    1. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    3. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    4. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    5. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting," Papers 2404.02671, arXiv.org, revised Nov 2024.
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    134. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    135. Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.
    136. Philip Hans Franses, 2021. "Marketing response and temporal aggregation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 111-117, June.

  45. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.
    See citations under working paper version above.
  46. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    See citations under working paper version above.
  47. Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2015. "Classical time varying factor-augmented vector auto-regressive models—estimation, forecasting and structural analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 493-533, June.

    Cited by:

    1. Sungurtekin Hallam, Bahar, 2022. "Emerging market responses to external shocks: A cross-country analysis," Economic Modelling, Elsevier, vol. 115(C).
    2. Riccardo Borghi & Eric Hillebrand & Jakob Mikkelsen & Giovanni Urga, 2018. "The dynamics of factor loadings in the cross-section of returns," CREATES Research Papers 2018-38, Department of Economics and Business Economics, Aarhus University.
    3. Mathias Klein & Ludger Linnemann, 2018. "Macroeconomic Effects of Government Spending: The Great Recession Was (Really) Different," Discussion Papers of DIW Berlin 1754, DIW Berlin, German Institute for Economic Research.
    4. Rudrani Bhattacharya & Parma Chakravartti & Sudipto Mundle, 2019. "Forecasting India’s economic growth: a time-varying parameter regression approach," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(3), pages 205-228, September.
    5. Massimo Guidolin & Manuela Pedio, 2019. "Does the Cost of Private Debt Respond to Monetary Policy? Heteroskedasticity-Based Identification in a Model with Regimes," BAFFI CAREFIN Working Papers 19118, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    6. Fu, Zhonghao & Hong, Yongmiao & Wang, Xia, 2023. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," Journal of Econometrics, Elsevier, vol. 233(1), pages 302-331.
    7. André Lucas & Julia Schaumburg & Bernd Schwaab, 2020. "Dynamic clustering of multivariate panel data," Tinbergen Institute Discussion Papers 20-009/III, Tinbergen Institute.
    8. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    9. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    10. Reza Najarzadeh & Alireza Keikha & Hassan Heydari, 2021. "Dynamics of consumption distribution and economic fluctuations," Economic Change and Restructuring, Springer, vol. 54(3), pages 847-876, August.
    11. Guidolin, Massimo & Hansen, Erwin & Pedio, Manuela, 2019. "Cross-asset contagion in the financial crisis: A Bayesian time-varying parameter approach," Journal of Financial Markets, Elsevier, vol. 45(C), pages 83-114.
    12. Ying Lun Cheung, 2024. "Identification of Time-Varying Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 76-94, January.
    13. Mikkelsen, Jakob Guldbæk & Hillebrand, Eric & Urga, Giovanni, 2019. "Consistent estimation of time-varying loadings in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 208(2), pages 535-562.
    14. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    15. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.
    16. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    17. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-switching three-pass regression filter," Working Papers 1748, Banco de España.
    18. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2018. "The global component of inflation volatility," Temi di discussione (Economic working papers) 1170, Bank of Italy, Economic Research and International Relations Area.
    19. Corradin, Stefano & Schwaab, Bernd, 2023. "Euro area sovereign bond risk premia before and during the Covid-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    20. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    21. Shikha Gupta & Nand Kumar, 2023. "Time varying dynamics of globalization effect in India," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 22(1), pages 81-97, January.
    22. Herwartz, Helmut & Rohloff, Hannes, 2018. "Less bang for the buck? Assessing the role of inflation uncertainty for U.S. monetary policy transmission in a data rich environment," University of Göttingen Working Papers in Economics 358, University of Goettingen, Department of Economics.
    23. Corradin, Stefano & Grimm, Niklas & Schwaab, Bernd, 2021. "Euro area sovereign bond risk premia during the Covid-19 pandemic," Working Paper Series 2561, European Central Bank.
    24. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
    25. Yanhong Feng & Dilong Xu & Pierre Failler & Tinghui Li, 2020. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation," Sustainability, MDPI, vol. 12(16), pages 1-24, August.
    26. Han, Xu, 2018. "Estimation and inference of dynamic structural factor models with over-identifying restrictions," Journal of Econometrics, Elsevier, vol. 202(2), pages 125-147.
    27. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    28. Juan S. Holguín & Jorge M. Uribe, 2020. "The credit supply channel of monetary policy: evidence from a FAVAR model with sign restrictions," Empirical Economics, Springer, vol. 59(5), pages 2443-2472, November.
    29. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    30. Masud Alam, 2024. "Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach," Empirical Economics, Springer, vol. 67(4), pages 1421-1471, October.
    31. Duván Humberto Cataño & Carlos Vladimir Rodríguez-Caballero & Daniel Peña, 2019. "Wavelet Estimation for Dynamic Factor Models with Time-Varying Loadings," CREATES Research Papers 2019-23, Department of Economics and Business Economics, Aarhus University.
    32. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    33. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    34. Paolo Gorgi & Siem Jan Koopman & Julia Schaumburg, 2021. "Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors," Tinbergen Institute Discussion Papers 21-056/III, Tinbergen Institute.

  48. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    See citations under working paper version above.
  49. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    See citations under working paper version above.
  50. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    See citations under working paper version above.
  51. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.

    Cited by:

    1. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    2. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Post-Print hal-03647097, HAL.
    3. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    4. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    5. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    6. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    7. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    8. Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
    9. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
    10. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
    11. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    12. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    13. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    14. Liu, Yang & Swanson, Norman R., 2024. "An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1391-1409.
    15. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
    16. Sagaert, Yves R. & Kourentzes, Nikolaos & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "Incorporating macroeconomic leading indicators in tactical capacity planning," International Journal of Production Economics, Elsevier, vol. 209(C), pages 12-19.
    17. Saulius Jokubaitis & Dmitrij Celov & Remigijus Leipus, 2019. "Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run," Papers 1906.07992, arXiv.org, revised Oct 2020.
    18. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    19. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    20. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    21. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
    22. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
    23. Christiana Anaxagorou & Nicoletta Pashourtidou, 2022. "Forecasting economic activity using preselected predictors: the case of Cyprus," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 16(1), pages 11-36, June.
    24. Luke Hartigan & Tom Rosewall, 2024. "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," RBA Research Discussion Papers rdp2024-04, Reserve Bank of Australia.
    25. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    26. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    27. Cláudia Duarte & Sónia Cabral, 2016. "Nowcasting Portuguese tourism exports," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    28. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.

  52. Galvao Ana Beatriz & Marcellino Massimiliano, 2014. "The effects of the monetary policy stance on the transmission mechanism," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 217-236, May.

    Cited by:

    1. KANAZAWA, Nobuyuki & 金澤, 伸幸, 2018. "Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks," Discussion paper series HIAS-E-64, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    2. Ana B. Galvão & Michael T. Owyang, 2014. "Financial stress regimes and the macroeconomy," Working Papers 2014-20, Federal Reserve Bank of St. Louis.
    3. Elif ERER & Deniz ERER & Mustafa ÇAYIR & Nasuh Oğuzhan ALTAY, 2016. "TCMB, FED ve ECB Para Politikalarının Türkiye Ekonomisi Üzerindeki Etkileri: 1994-2014 Dönemi Analizi," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
    4. Martin Bruns & Michele Piffer, 2021. "Monetary policy shocks over the business cycle: Extending the Smooth Transition framework," University of East Anglia School of Economics Working Paper Series 2021-07, School of Economics, University of East Anglia, Norwich, UK..

  53. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed‐Frequency Structural Models: Identification, Estimation, And Policy Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1118-1144, November.

    Cited by:

    1. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    2. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    3. Laurent Ferrara & Pierre Guérin, 2015. "What Are The Macroeconomic Effects of High-Frequency Uncertainty Shocks?," EconomiX Working Papers 2015-12, University of Paris Nanterre, EconomiX.
    4. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2021. "Measuring the effectiveness of US monetary policy during the COVID‐19 recession," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(3), pages 287-297, July.
    5. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    6. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2016. "Structural analysis with mixed frequencies: monetary policy, uncertainty and gross capital flows," JRC Working Papers in Economics and Finance 2016-04, Joint Research Centre, European Commission.
    7. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    8. Marcellino, Massimiliano & Foroni, Claudia & Casarin, Roberto & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    9. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    10. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    11. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.
    12. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    13. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    14. Ruey Yau & C. James Hueng, 2019. "Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 177-198, June.
    15. Stylianos Asimakopoulos & Marco Lorusso & Francesco Ravazzolo, 2019. "A New Economic Framework: A DSGE Model with Cryptocurrency," Working Papers No 07/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    16. Norberto Rodríguez-Niño & Alejandra Ramírez-Ramírez, 2018. "Metodologías semi-estructurales para estimar la Inflación básica mensual en Colombia," Borradores de Economia 1040, Banco de la Republica de Colombia.

  54. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
    See citations under working paper version above.
  55. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
    See citations under working paper version above.
  56. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.

    Cited by:

    1. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    2. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    3. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    4. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    5. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    6. Edmond H. C. Wu & Jihao Hu & Rui Chen, 2022. "Monitoring and forecasting COVID-19 impacts on hotel occupancy rates with daily visitor arrivals and search queries," Current Issues in Tourism, Taylor & Francis Journals, vol. 25(3), pages 490-507, February.
    7. Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
    8. Yang, Jianlei & Yang, Chunpeng, 2021. "The impact of mixed-frequency geopolitical risk on stock market returns," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 226-240.
    9. Götz, Thomas B. & Knetsch, Thomas A., 2017. "Google data in bridge equation models for German GDP," Discussion Papers 18/2017, Deutsche Bundesbank.
    10. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    11. Katja Heinisch & Rolf Scheufele, 2019. "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 170-200, November.
    12. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    13. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    14. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    15. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    16. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    17. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    18. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
    19. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    20. Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan Working Paper Series 18-E-18, Bank of Japan.
    21. Fornaro, Paolo, 2016. "Predicting Finnish economic activity using firm-level data," International Journal of Forecasting, Elsevier, vol. 32(1), pages 10-19.
    22. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    23. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    24. Joan Paredes & Javier J. Pérez & Gabriel Perez Quiros, 2023. "Fiscal targets. A guide to forecasters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 472-492, June.
    25. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    26. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    27. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    28. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    29. Alejandro Fernández Cerezo, 2023. "A supply-side GDP nowcasting model," Economic Bulletin, Banco de España, issue 2023/Q1.
    30. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    31. Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019. "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, vol. 15(2), pages 151-178, June.
    32. Jos Jansen, W. & Jin, Xiaowen & Winter, Jasper M. de, 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," Munich Reprints in Economics 43488, University of Munich, Department of Economics.
    33. Christian Glocker & Serguei Kaniovski, 2020. "Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models," WIFO Working Papers 614, WIFO.
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    35. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    36. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    37. Cláudia Duarte, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
    38. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    39. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    40. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, May.
    41. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    42. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    43. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    44. Gong, Xu & Sun, Yi & Du, Zhili, 2022. "Geopolitical risk and China's oil security," Energy Policy, Elsevier, vol. 163(C).
    45. Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021. "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    46. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
    47. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    48. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    49. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    50. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    51. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
    52. Lin, Jiahe & Michailidis, George, 2024. "A multi-task encoder-dual-decoder framework for mixed frequency data prediction," International Journal of Forecasting, Elsevier, vol. 40(3), pages 942-957.
    53. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," CEIS Research Paper 559, Tor Vergata University, CEIS, revised 15 Jun 2023.
    54. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    55. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    56. Emami Javanmard, Majid & Tang, Yili & Martínez-Hernández, J. Adrián, 2024. "Forecasting air transportation demand and its impacts on energy consumption and emission," Applied Energy, Elsevier, vol. 364(C).
    57. Alejo Estavillo & Gabriela Mordecki, 2023. "Nowcasting del PIB para Uruguay en base a un modelo de ecuaciones puente," Documentos de Trabajo (working papers) 23-26, Instituto de Economía - IECON.
    58. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW Kiel).
    59. Julian Ashwin & Eleni Kalamara & Lorena Saiz, 2024. "Nowcasting Euro area GDP with news sentiment: A tale of two crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 887-905, August.
    60. Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
    61. Christiana Anaxagorou & Nicoletta Pashourtidou, 2022. "Forecasting economic activity using preselected predictors: the case of Cyprus," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 16(1), pages 11-36, June.
    62. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
    63. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    64. Barış Soybilgen & M. Ege Yazgan & Hüseyin Kaya, 2023. "Nowcasting Turkish Food Inflation Using Daily Online Prices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 171-190, September.
    65. Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023. "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 213-234, March.
    66. Luke Hartigan & Tom Rosewall, 2024. "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," RBA Research Discussion Papers rdp2024-04, Reserve Bank of Australia.
    67. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    68. Pirschel, Inske, 2015. "Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113031, Verein für Socialpolitik / German Economic Association.
    69. Jack Fosten & Daniel Gutknecht, 2021. "Horizon confidence sets," Empirical Economics, Springer, vol. 61(2), pages 667-692, August.
    70. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    71. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    72. Andres Algaba & Samuel Borms & Kris Boudt & Brecht Verbeken, 2021. "Daily news sentiment and monthly surveys: A mixed–frequency dynamic factor model for nowcasting consumer confidence," Working Paper Research 396, National Bank of Belgium.
    73. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    74. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    75. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    76. Ruey Yau & C. James Hueng, 2019. "Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 177-198, June.
    77. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    78. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    79. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    80. Cláudia Duarte & Sónia Cabral, 2016. "Nowcasting Portuguese tourism exports," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    81. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.

  57. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.

    Cited by:

    1. Patrick Grüning & Ginters Buss, 2020. "Fiscal DSGE Model for Latvia," Bank of Lithuania Working Paper Series 81, Bank of Lithuania.
    2. Van Nguyen, Phuong, 2020. "Evaluating the forecasting accuracy of the closed- and open economy New Keynesian DSGE models," Dynare Working Papers 59, CEPREMAP.
    3. Stelios Bekiros & Alessia Paccagnini, 2013. "Policy-oriented macroeconomic forecasting with hybrid DGSE and time-varying parameter VAR models," Working Papers 236, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
    4. Marcin Kolasa & Michał Rubaszek, 2018. "Does the foreign sector help forecast domestic variables in DSGE models?," NBP Working Papers 282, Narodowy Bank Polski.
    5. Erlan Konebayev, 2022. "Forecasting a commodity-exporting small open developing economy using DSGE and DSGE-BVAR," NAC Analytica Working Paper 24, NAC Analytica, Nazarbayev University, revised May 2022.
    6. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2019. "Forecasting with instabilities: an application to DSGE models with financial frictions," Temi di discussione (Economic working papers) 1234, Bank of Italy, Economic Research and International Relations Area.
    7. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    8. Roberto Duncan & Enrique Martínez‐García, 2023. "Forecasting inflation in open economies: What can a NOEM model do?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 481-513, April.
    9. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2015. "Forecasting in a DSGE Model with Banking Intermediation: Evidence from the US," Working Papers 292, University of Milano-Bicocca, Department of Economics, revised Feb 2015.
    10. Fernando de Menezes Linardi, 2016. "Assessing the Fit of a Small Open-Economy DSGE Model for the Brazilian Economy," Working Papers Series 424, Central Bank of Brazil, Research Department.

  58. Bekiros, Stelios & Marcellino, Massimiliano, 2013. "The multiscale causal dynamics of foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 282-305.
    See citations under working paper version above.
  59. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2013. "Pooling Versus Model Selection For Nowcasting Gdp With Many Predictors: Empirical Evidence For Six Industrialized Countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 392-411, April.

    Cited by:

    1. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, May.
    2. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    3. Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter, 2014. "Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components," Tinbergen Institute Discussion Papers 14-113/III, Tinbergen Institute.
    4. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    5. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    6. Luci Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon M. Potter, 2014. "Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences," Staff Reports 680, Federal Reserve Bank of New York.
    7. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    8. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    9. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    10. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    11. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    12. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    13. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    14. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    15. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    16. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    17. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    18. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    19. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    20. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    21. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
    22. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    23. Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan Working Paper Series 18-E-18, Bank of Japan.
    24. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    25. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    26. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    27. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    28. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    29. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    30. Hwee Kwan Chow & Keen Meng Choy, 2023. "Economic forecasting in a pandemic: some evidence from Singapore," Empirical Economics, Springer, vol. 64(5), pages 2105-2124, May.
    31. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    32. Jos Jansen, W. & Jin, Xiaowen & Winter, Jasper M. de, 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," Munich Reprints in Economics 43488, University of Munich, Department of Economics.
    33. Christian Glocker & Serguei Kaniovski, 2020. "Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models," WIFO Working Papers 614, WIFO.
    34. Aleksandra Riedl & Julia Wörz, 2018. "A simple approach to nowcasting GDP growth in CESEE economies," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/18, pages 56-74.
    35. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    36. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    37. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    38. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
    39. Dorinth van Dijk & Jasper de Winter, 2023. "Nowcasting GDP using tone-adjusted time varying news topics: Evidence from the financial press," Working Papers 766, DNB.
    40. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    41. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    42. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    43. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2017. "Density Forecasts With Midas Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 783-801, June.
    44. Daniel Kaufmann & Rolf Scheufele, 2015. "Business tendency surveys and macroeconomic fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
    45. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    46. Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021. "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    47. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    48. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
    49. Baris Soybilgen & Ege Yazgan, 2017. "Nowcasting The New Turkish Gdp," Working Papers 1702, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    50. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    51. Hauber, Philipp, 2018. "Zur Kurzfristprognose mit Faktormodellen und Prognoseanpassungen," Kiel Insight 2018.5, Kiel Institute for the World Economy (IfW Kiel).
    52. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    53. Pirschel, Inske & Wolters, Maik, 2014. "Forecasting German key macroeconomic variables using large dataset methods," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100587, Verein für Socialpolitik / German Economic Association.
    54. Nikoleta Anesti & Ana Beatriz Galvão & Silvia Miranda‐Agrippino, 2022. "Uncertain Kingdom: Nowcasting Gross Domestic Product and its revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 42-62, January.
    55. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    56. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
    57. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
    58. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," CEIS Research Paper 559, Tor Vergata University, CEIS, revised 15 Jun 2023.
    59. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    60. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    61. Maxime Leboeuf & Louis Morel, 2014. "Forecasting Short-Term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions," Discussion Papers 14-3, Bank of Canada.
    62. Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Potjagailo, Galina, 2018. "Deutsche Konjunktur im Frühjahr 2018 - Deutsche Wirtschaft näher am Limit [German Economy Spring 2018 - German economy closer to its limit]," Kieler Konjunkturberichte 41, Kiel Institute for the World Economy (IfW Kiel).
    63. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    64. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    65. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    66. Jack Fosten & Daniel Gutknecht, 2021. "Horizon confidence sets," Empirical Economics, Springer, vol. 61(2), pages 667-692, August.
    67. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    68. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
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    71. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    72. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    73. Cláudia Duarte & Sónia Cabral, 2016. "Nowcasting Portuguese tourism exports," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    74. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.
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  60. Jordà, Òscar & Knüppel, Malte & Marcellino, Massimiliano, 2013. "Empirical simultaneous prediction regions for path-forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 456-468.
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  61. Pierre Guérin & Massimiliano Marcellino, 2013. "Markov-Switching MIDAS Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 45-56, January.
    See citations under working paper version above.
  62. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.

    Cited by:

    1. Alberto Caruso & Laura Coroneo, 2023. "Does Real‐Time Macroeconomic Information Help to Predict Interest Rates?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2027-2059, December.
    2. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    3. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    4. Joshua C. C. Chan, 2024. "BVARs and stochastic volatility," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 3, pages 43-67, Edward Elgar Publishing.
    5. Lewis N.K. Mambo, 2024. "From Multidimensional Ornstein - Uhlenbeck Process to Bayesian Vector Autoregressive Process," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 15(1), pages 1-32, December.
    6. Florian Huber & Gregor Kastner & Martin Feldkircher, 2016. "Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models," Papers 1607.04532, arXiv.org, revised Jul 2018.
    7. Andrea Carriero & Lorenzo Ricci & Elisabetta Vangelista, 2022. "Expectations and term premia in EFSF bond yields," Working Papers 54, European Stability Mechanism.
    8. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2010. "Forecasting with Factor-augmented Error Correction Models," CEPR Discussion Papers 7677, C.E.P.R. Discussion Papers.
    9. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    10. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage," CAMA Working Papers 2019-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Juan Antolin-Diaz & Ivan Petrella & Juan F. Rubio-Ramirez, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," FRB Atlanta Working Paper 2021-25, Federal Reserve Bank of Atlanta.
    12. Argyropoulos Efthymios & Tzavalis Elias, 2015. "Term spread regressions of the rational expectations hypothesis of the term structure allowing for risk premium effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 49-70, February.
    13. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," MPRA Paper 63844, University Library of Munich, Germany.
    14. Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Adaptive hierarchical priors for high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 212(1), pages 241-271.
    15. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    16. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2018. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2018-08, Department of Economics, University of Konstanz.
    18. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2023. "General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 69-87, January.
    19. Gregory R. Duffee, 2012. "Forecasting interest rates," Economics Working Paper Archive 599, The Johns Hopkins University,Department of Economics.
    20. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    21. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2014. "No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," CEPR Discussion Papers 9848, C.E.P.R. Discussion Papers.
    22. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    23. Gary Koop & Dimitris Korobilis, 2013. "A new index of financial conditions," Working Papers 1307, University of Strathclyde Business School, Department of Economics.
    24. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    25. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.
    26. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    27. Oskar Gustafsson & Mattias Villani & Pär Stockhammar, 2023. "Bayesian optimization of hyperparameters from noisy marginal likelihood estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 577-595, June.
    28. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    29. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
    30. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    31. Jan Pruser, 2024. "A large non-Gaussian structural VAR with application to Monetary Policy," Papers 2412.17598, arXiv.org.
    32. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    33. Shevelev A.A., 2017. "Bayesian approach to evaluate the impact of external shocks on Russian macroeconomics indicators," World of economics and management / Vestnik NSU. Series: Social and Economics Sciences, Socionet, vol. 17(1), pages 26-40.
    34. Almeida, Caio & Ardison, Kym & Kubudi, Daniela, 2014. "Approximating Risk Premium on a Parametric Arbitrage-free Term Structure Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 34(2), November.
    35. Andrea Carriero & Davide Pettenuzzo & Shubhranshu Shekhar, 2024. "Macroeconomic Forecasting with Large Language Models," Papers 2407.00890, arXiv.org, revised Mar 2025.
    36. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    37. William Gatt & Germano Ruisi, 2020. "Housing demand shocks, foreign labour inflows and consumption," CBM Working Papers WP/07/2020, Central Bank of Malta.
    38. Sebastian Ankargren & Måns Unosson & Yukai Yang, 2018. "A mixed-frequency Bayesian vector autoregression with a steady-state prior," CREATES Research Papers 2018-32, Department of Economics and Business Economics, Aarhus University.
    39. Prüser Jan & Hanck Christoph, 2021. "A Comparison of Approaches to Select the Informativeness of Priors in BVARs," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(4), pages 501-525, August.
    40. И Управления Мир Экономики, 2017. "Байесовский подход к анализу влияния монетарной политики на макроэкономические показатели России. Bayesian approach to the analysis of monetary policy impact on Russian macroeconomics indicators," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(4), pages 53-70.
    41. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    42. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
    43. Danilo Leiva-Leon, 2017. "Monitoring the Spanish Economy through the Lenses of Structural Bayesian VARs," Occasional Papers 1706, Banco de España.
    44. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.

  63. Demertzis Maria & Marcellino Massimiliano & Viegi Nicola, 2012. "A Credibility Proxy: Tracking US Monetary Developments," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-36, June.

    Cited by:

    1. Issler, João Victor & Soares, Ana Flávia, 2019. "Central Bank credibility and inflation expectations: a microfounded forecasting approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 812, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Łyziak, Tomasz & Paloviita, Maritta, 2018. "On the formation of inflation expectations in turbulent times: The case of the euro area," Economic Modelling, Elsevier, vol. 72(C), pages 132-139.
    3. Carlos Medel, 2018. "Econometric Analysis on Survey-data-based Anchoring of Inflation Expectations in Chile," Working Papers Central Bank of Chile 825, Central Bank of Chile.
    4. Aßhoff, Sina & Belke, Ansgar & Osowski, Thomas, 2021. "Unconventional monetary policy and inflation expectations in the Euro area," Economic Modelling, Elsevier, vol. 102(C).
    5. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2019. "Expectations Anchoring Indexes for Brazil using Kalman Filter: exploring signals of inflation anchoring in the long term," Working Papers Series 497, Central Bank of Brazil, Research Department.
    6. Bems, Rudolfs & Caselli, Francesca & Grigoli, Francesco & Gruss, Bertrand, 2021. "Expectations' anchoring and inflation persistence," Journal of International Economics, Elsevier, vol. 132(C).
    7. Mark K. Armah, 2024. "The effect of central bank credibility on economic growth and output volatility in inflation targeting regime," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 26(2), pages 619-640, August.
    8. Mariana Colacelli & Emilio Fernández Corugedo, 2018. "Macroeconomic Effects of Japan’s Demographics: Can Structural Reforms Reverse Them?," IMF Working Papers 2018/248, International Monetary Fund.
    9. Cem Cakmakli & Selva Demiralp, 2020. "A Dynamic Evaluation of Central Bank Credibility," Koç University-TUSIAD Economic Research Forum Working Papers 2015, Koc University-TUSIAD Economic Research Forum.
    10. van der Cruijsen, Carin & Demertzis, Maria, 2011. "How anchored are inflation expectations in EMU countries?," Economic Modelling, Elsevier, vol. 28(1), pages 281-298.
    11. Nicolas End, 2020. "Rousseau's social contract or Machiavelli's virtue? A measure of fiscal credibility," AMSE Working Papers 2042, Aix-Marseille School of Economics, France.
    12. Oinonen, Sami & Paloviita, Maritta & Viren, Matti, 2018. "Effects of monetary policy decisions on professional forecasters' expectations and expectations uncertainty," Bank of Finland Research Discussion Papers 24/2018, Bank of Finland.
    13. Bems, Rudolfs & Caselli, Francesca & Grigoli, Francesco & Gruss, Bertrand, 2020. "Gains from anchoring inflation expectations: Evidence from the taper tantrum shock," Economics Letters, Elsevier, vol. 188(C).
    14. Alberto Coco & Nicola Viegi, 2020. "The monetary policy of the South African Reserve Bank stance communication and credibility," Working Papers 10024, South African Reserve Bank.
    15. Peter Jorgensen & Kevin J. Lansing, 2024. "A Simple Measure of Anchoring for Short-Run Expected Inflation in FIRE Models," Working Paper Series 2024-34, Federal Reserve Bank of San Francisco.
    16. End, Nicolas, 2023. "Big Brother is also being watched: Measuring fiscal credibility," Journal of Macroeconomics, Elsevier, vol. 77(C).
    17. Strohsal, Till & Melnick, Rafi & Nautz, Dieter, 2016. "The time-varying degree of inflation expectations anchoring," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 62-71.
    18. Anderson Grajales-Olarte & Franz Hamann & Sara Naranjo-Saldarriaga & José Pulido, 2025. "Policy implications of losing credibility: Lessons from Colombia’s post-pandemic inflationary surge," Borradores de Economia 1304, Banco de la Republica de Colombia.
    19. Kristoph Naggert & Robert W. Rich & Joseph Tracy, 2023. "The Anchoring of US Inflation Expectations Since 2012," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2023(11), pages 1-7, July.
    20. Jonas Dovern & Geoff Kenny, 2020. "Anchoring Inflation Expectations in Unconventional Times: Micro Evidence for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 16(5), pages 309-347, October.
    21. Mayes, David G. & Paloviita, Maritta & Virén, Matti, 2016. "EMU and the Anchoring of Inflation Expectations," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 69(4), pages 341-364.
    22. Anuradha Patnaik, 2025. "Are inflation expectations anchored in India? A peek through the credibility lens," International Economics and Economic Policy, Springer, vol. 22(1), pages 1-23, February.
    23. Bicchal, Motilal, 2022. "Central bank credibility and its effect on stabilization," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 73-94.
    24. Kenny, Geoff & Dovern, Jonas, 2017. "The long-term distribution of expected inflation in the euro area: what has changed since the great recession?," Working Paper Series 1999, European Central Bank.
    25. Dash, Pradyumna & Rohit, Abhishek Kumar & Devaguptapu, Adviti, 2020. "Assessing the (de-)anchoring of households’ long-term inflation expectations in the US," Journal of Macroeconomics, Elsevier, vol. 63(C).
    26. Gießler, Stefan, 2020. "The evolution of monetary policy in Latin American economies: Responsiveness to inflation under different degrees of credibility," IWH Discussion Papers 9/2020, Halle Institute for Economic Research (IWH).

  64. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2011. "EUROMIND: a monthly indicator of the euro area economic conditions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 439-470, April.

    Cited by:

    1. Cecilia Frale, Serena Teobaldo, Marco Cacciotti, Alessandra Caretta, 2013. "A Quarterly Measure Of Potential Output In The New European Fiscal Framework," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    2. Martyna Marczak & Víctor Gómez, 2017. "Monthly US business cycle indicators: a new multivariate approach based on a band-pass filter," Empirical Economics, Springer, vol. 52(4), pages 1379-1408, June.
    3. Michele Modugno & Lucrezia Reichlin & Domenico Giannone & Marta Banbura, 2012. "Nowcasting with Daily Data," 2012 Meeting Papers 555, Society for Economic Dynamics.
    4. Donato Ceci & Andrea Silvestrini, 2023. "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.
    5. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    6. Katja Drechsel & Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    7. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    8. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    9. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    11. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    12. Marco Cacciotti & Cecilia Frale & Serena Teobaldo, 2013. "A new methodology for a quarterly measure of the output gap," Working Papers 6, Department of the Treasury, Ministry of the Economy and of Finance.
    13. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    14. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    15. Mokinski, Frieder, 2016. "Using time-stamped survey responses to measure expectations at a daily frequency," International Journal of Forecasting, Elsevier, vol. 32(2), pages 271-282.
    16. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    17. Tommaso Proietti & Alessandra Luati, 2013. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 15, pages 334-362, Edward Elgar Publishing.
    18. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
    19. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    20. Tommaso Proietti & Martyna Marczak & Gianluigi Mazzi, 2015. "EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area," CEIS Research Paper 340, Tor Vergata University, CEIS, revised 10 Apr 2015.
    21. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    22. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    23. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
    24. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    25. Moauro, Filippo, 2010. "A monthly indicator of employment in the euro area: real time analysis of indirect estimates," MPRA Paper 27797, University Library of Munich, Germany, revised 30 Dec 2010.
    26. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    27. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    28. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    29. Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2024. "Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates," International Journal of Forecasting, Elsevier, vol. 40(2), pages 626-640.
    30. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    31. Raïsa Basselier & David Antonio Liedo & Geert Langenus, 2018. "Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 1-46, April.
    32. Donatella Baiardi & Carluccio Bianchi, 2012. "Un Indicatore per la Lombardia e per le Province di Milano e Pavia (Nuova versione)," Quaderni di Dipartimento 158, University of Pavia, Department of Economics and Quantitative Methods.
    33. D’Elia Enrico, 2014. "Predictions vs. Preliminary Sample Estimates: The Case of Eurozone Quarterly GDP," Journal of Official Statistics, Sciendo, vol. 30(3), pages 499-520, September.
    34. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    35. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," CEIS Research Paper 559, Tor Vergata University, CEIS, revised 15 Jun 2023.
    36. Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021. "Forecasting the production side of GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
    37. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    38. Juan Pablo Cote-Barón & Karen L. Pulido-Mahecha & Nicol Valeria Rodríguez-Rodríguez & Carlos D. Rojas-Martínez, 2023. "El ISAE: Un Indicador para Monitorear la Actividad Económica Colombiana en Alta Frecuencia," Borradores de Economia 1225, Banco de la Republica de Colombia.
    39. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    40. Ramazan Yanik & Asfia Binte Osman & Ozcan Ozturk, 2020. "Impact of manufacturing PMI on stock market index: A study on Turkey," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 6(3), pages 104-108.
    41. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    42. Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.
    43. Barhoumi, K. & Darn , O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    44. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.

  65. Deák, Szabolcs & Fontagné, Lionel & Maffezzoli, Marco & Marcellino, Massimiliano, 2011. "LSM: A DSGE model for Luxembourg," Economic Modelling, Elsevier, vol. 28(6), pages 2862-2872.
    See citations under working paper version above.
  66. Marcellino, Massimiliano & Musso, Alberto, 2011. "The reliability of real-time estimates of the euro area output gap," Economic Modelling, Elsevier, vol. 28(4), pages 1842-1856, July.
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  67. Andrea Carriero & Massimiliano Marcellino, 2011. "Sectoral Survey‐based Confidence Indicators for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 175-206, April.
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    143. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    144. Pirschel, Inske, 2015. "Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113031, Verein für Socialpolitik / German Economic Association.
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    152. Andrey Zubarev & Daniil Lomonosov & Konstantin Rybak, 2022. "Estimation of the Impact of Global Shocks on the Russian Economy and GDP Nowcasting Using a Factor Model," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 49-78, June.
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  75. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
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    1. de Groot, Oliver & Hauptmeier, Sebastian & Holm-Hadulla, Fédéric & Nikalexi, Katerina, 2020. "Monetary policy and regional inequality," Working Paper Series 2385, European Central Bank.
    2. Oguz Atuk & Cem Aysoy & Mustafa Utku Ozmen & Cagri Sarikaya, 2014. "Turkiye�de Enflasyonun Is Cevrimlerine Duyarliligi : Cikti Acigina Duyarli TUFE Alt Gruplarinin Saptanmasi," Working Papers 1437, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    3. Poncela, Pilar, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Hasan Engin Duran & Pawe³ Gajewski, 2023. "State-level Taylor rule and monetary policy stress," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(1), pages 89-120, March.
    5. Virginie Coudert & Cécile Couharde & Valérie Mignon, 2012. "On currency misalignments within the euro area," Post-Print hal-01411726, HAL.
    6. Holger Zemanek & Ansgar Belke & Gunther Schnabl, 2010. "Current account balances and structural adjustment in the euro area," International Economics and Economic Policy, Springer, vol. 7(1), pages 83-127, May.
    7. Elena Deryugina & Natalia Karlova & Alexey Ponomarenko & Anna Tsvetkova, 2019. "The role of regional and sectoral factors in Russian inflation developments," Economic Change and Restructuring, Springer, vol. 52(4), pages 453-474, November.
    8. Petr Janský & Dominika Kolcunová, 2017. "Regional differences in price levels across the European Union and their implications for its regional policy," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(3), pages 641-660, May.
    9. Zemanek, Holger & Belke, Ansgar H. & Schnabl, Gunther, 2009. "Current Account Imbalances and Structural Adjustment in the Euro Area: How to Rebalance Competitiveness," IZA Policy Papers 7, Institute of Labor Economics (IZA).
    10. Nagayasu, Jun, 2010. "Regional Inflation (Price) Behaviors: Heterogeneity and Convergence," MPRA Paper 25430, University Library of Munich, Germany.
    11. Diego Winkelried & José Enrique Gutierrez, 2015. "Regional Inflation Dynamics and Inflation Targeting. The Case of Peru," Journal of Applied Economics, Taylor & Francis Journals, vol. 18(2), pages 199-224, November.
    12. Ogrokhina, Olena, 2019. "Persistence of prices in the Eurozone capital cities: Evidence from the Economist Intelligence Unit City Data," Economic Modelling, Elsevier, vol. 76(C), pages 330-338.
    13. Chatterjee, Arpita, 2016. "Globalization and monetary policy comovement: International evidence," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 181-202.
    14. Yanli LI, Hongfeng PENG & Hongfeng PENG, 2013. "Inflation Persistence in Nine Latin American Countries: Panel SURKSS Test with a Fourier Function," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 132-143, October.
    15. Mª Ángeles Caraballo & Tilemahos Efthimiadis, 2012. "Divergent Optimal Inflation Rates In Euro Area Countries Or "Does One Size Fit All?"," EcoMod2012 3923, EcoMod.
    16. St phane Lhuissier & Aymeric Ortmans & Fabien Tripier, 2024. "The Risk of Inflation Dispersion in the Euro Area," Working papers 954, Banque de France.
    17. van der Cruijsen, Carin & Demertzis, Maria, 2011. "How anchored are inflation expectations in EMU countries?," Economic Modelling, Elsevier, vol. 28(1), pages 281-298.
    18. Nagayasu, Jun, 2011. "Heterogeneity and convergence of regional inflation (prices)," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 711-723.
    19. Mongelli, Francesco Paolo & Reinhold, Elisa & Papadopoulos, Georgios, 2016. "What's so special about specialization in the euro area?," Occasional Paper Series 168, European Central Bank.
    20. Alejandro C. García-Cintado & Diego Romero-Ávila & Carlos Usabiaga, 2016. "The economic integration of Spain: a change in the inflation pattern," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 25(1), pages 1-41, December.
    21. Solórzano, Diego, 2023. "Heterogeneous exchange rate pass-through in Mexico: What drives it?," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(4).
    22. Francis Neville & Owyang Michael T. & Sekhposyan Tatevik, 2012. "The Local Effects of Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(2), pages 1-38, March.
    23. Matěj Bajgar & Petr Janský, 2015. "Skutečná kupní síla v krajích České republiky: zohlednění regionální cenové hladiny a struktury pracovní síly [Purchasing Power in the Regions: Reflecting Price Levels and Employment Structures]," Politická ekonomie, Prague University of Economics and Business, vol. 2015(7), pages 860-876.
    24. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    25. Arpita Chatterjee, 2014. "Globalization and Monetary Policy Comovement: Evidence from G-7 Countries," Discussion Papers 2014-19, School of Economics, The University of New South Wales.
    26. Eder, Andreas & Koller, Wolfgang & Mahlberg, Bernhard, 2019. "Price Competitiveness in the European Monetary Union: A Decomposition of Inflation Differentials based on the Leontief Input-Output Price Model for the Period 2000 to 2014," MPRA Paper 95158, University Library of Munich, Germany.
    27. Paweł Gajewski, 2017. "Sources of Regional Inflation in Poland," Eastern European Economics, Taylor & Francis Journals, vol. 55(3), pages 261-276, May.
    28. Harry Aginta, 2022. "Spatiotemporal analysis of regional inflation in an emerging country: The case of Indonesia," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(3), pages 667-688, June.
    29. Aviral Tiwari & Niyati Bhanja & Arif Dar & Olaolu Olayeni, 2015. "Analyzing Time–Frequency Based Co-movement in Inflation: Evidence from G-7 Countries," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 91-109, January.
    30. Martin Brown & Ralph De Haas & Vladimir Sokolov, 2018. "Regional Inflation, Banking Integration, and Dollarization [On the unstable relationship between exchange rates and macroeconomic fundamentals]," Review of Finance, European Finance Association, vol. 22(6), pages 2073-2108.
    31. Glocker, Christian & Wegmüller, Philipp, 2024. "Energy price surges and inflation: Fiscal policy to the rescue?," Journal of International Money and Finance, Elsevier, vol. 149(C).
    32. Neely, Christopher J. & Rapach, David E., 2011. "International comovements in inflation rates and country characteristics," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1471-1490.
    33. Jean Louis, Rosmy & Balli, Faruk, 2013. "Low-inflation-targeting monetary policy and differential unemployment rate: Is monetary policy to be blamed for the financial crisis? — Evidence from major OECD countries," Economic Modelling, Elsevier, vol. 30(C), pages 546-564.
    34. Harry Aginta, 2021. "Spatial dynamics of consumer price in Indonesia: convergence clubs and conditioning factors," Asia-Pacific Journal of Regional Science, Springer, vol. 5(2), pages 427-451, June.
    35. Kjetil Martinsen & Francesco Ravazzolo & Fredrik Wulfsberg, 2011. "Forecasting macroeconomic variables using disaggregate survey data," Working Paper 2011/04, Norges Bank.
    36. Chen, Changsheng & Girardin, Eric & Mehrotra, Aaron, 2017. "Global slack and open economy Phillips curves – A province-level view from China," China Economic Review, Elsevier, vol. 42(C), pages 74-87.
    37. Coleman, Simeon, 2012. "Where Does the Axe Fall? Inflation Dynamics and Poverty Rates: Regional and Sectoral Evidence for Ghana," World Development, Elsevier, vol. 40(12), pages 2454-2467.
    38. Christina Bräuning & Ralf Fendel, 2018. "National information and euro area monetary policy: a generalized ordered choice approach," Empirical Economics, Springer, vol. 54(2), pages 501-522, March.
    39. Shu-hen Chiang, 2016. "Rising residential rents in Chinese mega cities: The role of monetary policy," Urban Studies, Urban Studies Journal Limited, vol. 53(16), pages 3493-3509, December.
    40. Polina S. Kuklinova & Vladimir V. Ilyashenko, 2022. "The impact of the inflation-targeting regime on the economic development of an industrial region," Journal of New Economy, Ural State University of Economics, vol. 23(2), pages 125-141, July.
    41. Ivan F Dumka, 2016. "Coordinated wage setting and social partnership under EMU. A framework for analysis and results from Belgium, Germany and the Netherlands," Transfer: European Review of Labour and Research, , vol. 22(4), pages 445-460, November.
    42. Quint, Dominic, 2014. "How Large Is the Stress from the Common Monetary Policy in the Euro Area?," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100341, Verein für Socialpolitik / German Economic Association.
    43. Alyona Nelyubina, 2021. "Forecasting Regional Indicators Based on the Quarterly Projection Model," Russian Journal of Money and Finance, Bank of Russia, vol. 80(2), pages 50-75, June.
    44. van Ewijk, Saskia E. & Arnold, Ivo J.M., 2015. "Financial integration in the euro area: Pro-cyclical effects and economic convergence," Economic Modelling, Elsevier, vol. 44(C), pages 335-342.
    45. Gent Bajraj & Guillermo Carlomagno & Juan M. Wlasiuk, 2023. "Where is the Inflation? The Diverging Patterns of Prices of Goods and Services," Working Papers Central Bank of Chile 969, Central Bank of Chile.
    46. Mrs. Hanan Morsy & Ms. Florence Jaumotte, 2012. "Determinants of Inflation in the Euro Area: The Role of Labor and Product Market Institutions," IMF Working Papers 2012/037, International Monetary Fund.
    47. Quint, Dominic, 2014. "Is it really more dispersed? Measuring and comparing the stress from the common monetary policy in the euro area," Discussion Papers 2014/13, Free University Berlin, School of Business & Economics.
    48. International Monetary Fund, 2011. "Spain: Selected Issues," IMF Staff Country Reports 2011/216, International Monetary Fund.
    49. Dominic Quint, 2016. "Is it really more dispersed?," International Economics and Economic Policy, Springer, vol. 13(4), pages 593-621, October.
    50. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.
    51. Nikolsko-Rzhevskyy, Alex & Ogrokhina, Olena, 2018. "Behavior of retail prices in common currency areas: The case of the Eurozone," Economic Modelling, Elsevier, vol. 69(C), pages 49-57.
    52. Burridge, Peter & Iacone, Fabrizio & Lazarová, Štěpána, 2015. "Spatial effects in a common trend model of US city-level CPI," Regional Science and Urban Economics, Elsevier, vol. 54(C), pages 87-98.

  79. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2008. "Factor analysis in a model with rational expectations," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 271-286, July.
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  80. David F. Hendry & Massimiliano Marcellino & Grayham E. Mizon, 2008. "Guest Editors’ Introduction to Special Issue on Encompassing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 715-719, December.

    Cited by:

    1. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    2. D. R. Cox, 2013. "A return to an old paper: ‘Tests of separate families of hypotheses’," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 207-215, March.

  81. Massimiliano Marcellino & Barbara Rossi, 2008. "Model Selection for Nested and Overlapping Nonlinear, Dynamic and Possibly Mis‐specified Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 867-893, December.

    Cited by:

    1. Gospodinov, Nikolay & Kan, Raymond & Robotti, Cesare, 2013. "Chi-squared tests for evaluation and comparison of asset pricing models," Journal of Econometrics, Elsevier, vol. 173(1), pages 108-125.
    2. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    3. Christophe Bontemps & Grayham E. Mizon, 2008. "Encompassing: Concepts and Implementation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 721-750, December.
    4. Lavergne, Pascal & Bertail, Patrice, 2020. "Bootstrapping Quasi Likelihood Ratio Tests under Misspecification," TSE Working Papers 20-1102, Toulouse School of Economics (TSE).
    5. Bu Ruijun & Cheng Jie & Hadri Kaddour, 2017. "Specification analysis in regime-switching continuous-time diffusion models for market volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 65-80, February.
    6. Francesco Battaglia & Mattheos Protopapas, 2012. "An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 315-334, August.

  82. Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2008. "Forecasting euro area variables with German pre-EMU data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 465-481.
    See citations under working paper version above.
  83. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.

    Cited by:

    1. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    2. Lauren Hackler & Frank Hefner & Mark D. Witte, 2020. "The Effects of IMF Loan Condition Compliance on GDP Growth," The American Economist, Sage Publications, vol. 65(1), pages 88-96, March.
    3. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    4. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    5. Arabinda Basistha & Richard Startz, 2023. "Measuring Persistent Global Economic Factors with Output, Commodity Price, and Commodity Currency Data," Working Papers 23-05, Department of Economics, West Virginia University.
    6. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    7. Basistha, Arabinda & Kurov, Alexander & Wolfe, Marketa Halova, 2019. "Volatility Forecasting: The Role of Internet Search Activity and Implied Volatility," MPRA Paper 111037, University Library of Munich, Germany.
    8. Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
    9. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    10. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    11. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    12. Khan, Md. Tareq Ferdous & Kundu, Nobinkhor, 2012. "Future Contribution of Export and Import to GDP in Bangladesh: A Box-Jenkins Approach," MPRA Paper 65153, University Library of Munich, Germany, revised 15 Jun 2012.
    13. Kurmaş Akdoğan, 2019. "Size and sign asymmetries in house price adjustments," Applied Economics, Taylor & Francis Journals, vol. 51(48), pages 5268-5281, October.
    14. Blerina Vika & Ilir Vika, 2021. "Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 10, September.
    15. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Forecasting GDP with energy series: ADL-MIDAS vs. Linear Time Series Models," Working Papers 035, Centre for Econometric and Allied Research, University of Ibadan.
    16. Carstensen Kai & Wohlrabe Klaus & Ziegler Christina, 2011. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 82-106, February.
    17. Jason Furman, 2022. "Why Did (Almost) No One See the Inflation Coming?," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(2), pages 79-86, March.
    18. Milena Lipovina-Božović, 2013. "A Comparison Of The Var Model And The Pc Factor Model In Forecasting Inflation In Montenegro," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 58(198), pages 115-136, July - Se.
    19. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
    20. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    21. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    22. Akdoğan, Kurmaş, 2020. "Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?," Resources Policy, Elsevier, vol. 67(C).
    23. Sri Hasnawati & Mustofa Usman & Faiz AM Elfaki & Ahmad Faisol & Edwin Russel, 2024. "Modeling the Relationship between Life Expectancy, Population Growth, Carbon Dioxide Emission, and GDP Growth in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 484-500, July.
    24. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    25. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    26. Cobb, Marcus P A, 2017. "Forecasting Economic Aggregates Using Dynamic Component Grouping," MPRA Paper 81585, University Library of Munich, Germany.
    27. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Jean-Francois Richard, 2008. "Exploiting Non-Linearities in GDP Growth for Forecasting and Anticipating Regime Changes," Working Paper 367, Department of Economics, University of Pittsburgh, revised Sep 2008.
    28. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.

  84. Dreger, Christian & Marcellino, Massimiliano, 2007. "A macroeconometric model for the Euro economy," Journal of Policy Modeling, Elsevier, vol. 29(1), pages 1-13.

    Cited by:

    1. Alberto Bagnai & Christian Alexander Mongeau Ospina, 2014. "The a/simmetrie annual macroeconometric model of the Italian economy: structure and properties," a/ Working Papers Series 1405, Italian Association for the Study of Economic Asymmetries, Rome (Italy).
    2. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    3. Albacete, Rebeca, 2005. "Forecasting inflation in the euro area using monthly time series models and quarterly econometric models," DES - Working Papers. Statistics and Econometrics. WS ws050401, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Dreger, Christian & Reimers, Hans-Eggert, 2013. "Does euro area membership affect the relation between GDP growth and public debt?," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 481-486.
    5. Qiao, Zhuo & Chu, Patrick Kuok-Kun, 2014. "Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries," Economic Modelling, Elsevier, vol. 38(C), pages 75-79.
    6. Aizhan Bolatbayeva & Alisher Tolepbergen & Nurdaulet Abilov, 2020. "A macroeconometric model for Russia," Russian Journal of Economics, ARPHA Platform, vol. 6(2), pages 114-143, June.
    7. Klein, Lawrence R. & Kushnirsky, Fyodor I. & Maksymenko, Svitlana V., 2012. "Macroeconometric study of Ukraine's growth and reform," Journal of Policy Modeling, Elsevier, vol. 34(3), pages 325-340.
    8. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    9. Barabas, György & Gebhardt, Heinz & Münch, Heinz Josef & Schmidt, Christoph M. & Schmidt, Torsten & Breitung, Jörg, 2005. "Methoden mittelfristiger gesamtwirtschaftlicher Projektionen: Dienstleistungsvorhaben im Auftrag des Bundesministeriums für Wirtschaft und Arbeit, Projektnummer 02/05. Vorläufiger Endbericht," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 69948, November.
    10. Akbar, Muhammad & Ahmad, Eatzaz, 2021. "Repercussions of exchange rate depreciation on the economy of Pakistan: Simulation analysis using macroeconometric model," Journal of Policy Modeling, Elsevier, vol. 43(3), pages 574-600.
    11. Scheufele, Rolf, 2008. "Das makroökonometrische Modell des IWH: Eine angebotsseitige Betrachtung," IWH Discussion Papers 9/2008, Halle Institute for Economic Research (IWH).
    12. Fakhri J. Hasanov & Noha Razek, 2023. "Oil and Non-Oil Determinants of Saudi Arabia’s International Competitiveness: Historical Analysis and Policy Simulations," Sustainability, MDPI, vol. 15(11), pages 1-39, June.
    13. Jérôme Creel & Bruno Ducoudre & Catherine Mathieu & Henri Sterdyniak, 2005. "Doit-on oublier la politique budgétaire? Une analyse critique de la nouvelle théorie anti-keynésienne des finances publiques," SciencePo Working papers Main hal-01020610, HAL.
    14. Pierre-Olivier Beffy & Xavier Bonnet & Brieuc Monfort & Matthieu Darracq-Pariès & Jérôme Henry, 2003. "MZE, un modèle macroéconométrique pour la zone euro ; suivi d'un commentaire de Jérome Henry," Économie et Statistique, Programme National Persée, vol. 367(1), pages 3-37.
    15. Senra, Eva, 2017. "22 Years of inflation assessment and forecasting experience at the bulletin of EU & US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS 24678, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Dreger, Christian & Zhang, Yanqun, 2014. "Does the economic integration of China affect growth and inflation in industrial countries?," Economic Modelling, Elsevier, vol. 38(C), pages 184-189.
    17. Christian Dreger & Florian Zinsmeister, 2007. "Das IMM: ein makroökonometrisches Mehrländermodell," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 76(4), pages 35-46.
    18. Olofin, S.O. & Olubusoye, O.E. & Mordi, C.N.O. & Salisu, A.A. & Adeleke, A.I. & Orekoya, S.O. & Olowookere, A.E. & Adebiyi, M.A., 2014. "A small macroeconometric model of the Nigerian economy," Economic Modelling, Elsevier, vol. 39(C), pages 305-313.
    19. Olofin, S.O. & Salisu, A.A & Tule, M.K, 2020. "Revised Small Macro-Econometric Model Of The Nigerian Economy," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 20(1), pages 97-116.
    20. Villaverde, José & Maza, Adolfo, 2009. "The robustness of Okun's law in Spain, 1980-2004: Regional evidence," Journal of Policy Modeling, Elsevier, vol. 31(2), pages 289-297.
    21. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
    22. Salami, Habibollah & Shahnooshi, Naser & Thomson, Kenneth J., 2009. "The economic impacts of drought on the economy of Iran: An integration of linear programming and macroeconometric modelling approaches," Ecological Economics, Elsevier, vol. 68(4), pages 1032-1039, February.

  85. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    See citations under working paper version above.
  86. Ana Beatriz Galvão & Michael Artis & Massimiliano Marcellino, 2007. "The transmission mechanism in a changing world," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 39-61.
    See citations under working paper version above.
  87. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    See citations under working paper version above.
  88. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    See citations under working paper version above.
  89. Corielli, Francesco & Marcellino, Massimiliano, 2006. "Factor based index tracking," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2215-2233, August.
    See citations under working paper version above.
  90. Angelini, Elena & Henry, Jerome & Marcellino, Massimiliano, 2006. "Interpolation and backdating with a large information set," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2693-2724, December.
    See citations under working paper version above.
  91. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    See citations under working paper version above.
  92. Marcellino, Massimiliano, 2006. "Some stylized facts on non-systematic fiscal policy in the Euro area," Journal of Macroeconomics, Elsevier, vol. 28(3), pages 461-479, September.
    See citations under working paper version above.
  93. Carlo A. Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 755-783, December.
    See citations under working paper version above.
  94. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    See citations under working paper version above.
  95. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.
    See citations under working paper version above.
  96. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2005. "Testing for PPP: Should we use panel methods?," Empirical Economics, Springer, vol. 30(1), pages 77-91, January.
    See citations under working paper version above.
  97. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2005. "Business Cycles in the New EU Member Countries and their Conformity with the Euro Area," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 7-41.

    Cited by:

    1. Michael Artis, 2006. "What Do we Now Know About Currency Unions?," Economie Internationale, CEPII research center, issue 107, pages 9-28.
    2. Zsolt Darvas & György Szapáry, 2008. "Business Cycle Synchronization in the Enlarged EU," Open Economies Review, Springer, vol. 19(1), pages 1-19, February.
    3. Michael J. Artis & Jarko Fidrmuc & Johann Scharler, 2008. "The transmission of business cycles Implications for EMU enlargement1," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 16(3), pages 559-582, July.
    4. Camacho, Maximo & Perez-Quiros, Gabriel & Saiz, Lorena, 2008. "Do European business cycles look like one?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2165-2190, July.
    5. Oscar Bajo-Rubio & Carmen Díaz-Roldán, 2005. "Characterizing macroeconomic shocks in the CEECs," Economic Change and Restructuring, Springer, vol. 38(3), pages 227-234, December.
    6. Sandra Eickmeier & Joerg Breitung, 2006. "Business cycle transmission from the euro area to CEECs," Computing in Economics and Finance 2006 229, Society for Computational Economics.
    7. Haan, Jakob de & Jacobs, Jan P.A.M. & Zijm, Renske, 2024. "Coherence of the business cycles of prospective members of the euro area and the euro area business cycle," Economic Systems, Elsevier, vol. 48(4).
    8. Macchiarelli, Corrado, 2013. "GDP-Inflation cyclical similarities in the CEE countries and the euro area," Working Paper Series 1552, European Central Bank.
    9. Eickmeier, Sandra & Breitung, Jorg, 2006. "How synchronized are new EU member states with the euro area? Evidence from a structural factor model," Journal of Comparative Economics, Elsevier, vol. 34(3), pages 538-563, September.
    10. Bekiros Stelios & Nguyen Duc Khuong & Uddin Gazi Salah & Sjö Bo, 2015. "Business cycle (de)synchronization in the aftermath of the global financial crisis: implications for the Euro area," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 609-624, December.
    11. Gächter, Simon & Riedl, Alesandra & Ritzberger-Grünwald, Doris, 2013. "Business cycle convergence or decoupling? Economic adjustment in CESEE during the crisis," BOFIT Discussion Papers 3/2013, Bank of Finland Institute for Emerging Economies (BOFIT).
    12. Wasim Ahmad & N. Bhanumurthy & Sanjay Sehgal, 2015. "Regime dependent dynamics and European stock markets: Is asset allocation really possible?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(1), pages 77-107, February.
    13. Carlo Di Giorgio, 2016. "Business Cycle Synchronization of CEECs with the Euro Area: A Regime Switching Approach," Journal of Common Market Studies, Wiley Blackwell, vol. 54(2), pages 284-300, March.
    14. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2013. "Business cycle convergence or decoupling? Economic adjustment of CESEE countries during the crisis," Chapters, in: Ewald Nowotny & Peter Mooslechner & Doris Ritzberger-Grünwald (ed.), A New Model for Balanced Growth and Convergence, chapter 10, pages 147-169, Edward Elgar Publishing.

  98. Òscar Jordà & Massimiliano Marcellino, 2004. "Time‐scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.
    See citations under working paper version above.
  99. Massimiliano Marcellino, 2004. "Forecast Pooling for European Macroeconomic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 91-112, February.

    Cited by:

    1. Maurin, Laurent & Drechsel, Katja, 2008. "Flow of conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank.
    2. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
    3. MOULIN, Laurent & SALTO, Matteo & SILVESTRINI, Andrea & VEREDAS, David, 2004. "Using intra annual information to forecast the annual state deficits : the case of France," LIDAM Discussion Papers CORE 2004048, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. David E. Rapach & Jack K. Strauss, 2008. "Forecasting US employment growth using forecast combining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 75-93.
    5. Di Fonzo, Tommaso & Girolimetto, Daniele, 2024. "Forecast combination-based forecast reconciliation: Insights and extensions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 490-514.
    6. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
    8. Giancarlo Lutero & Marco Marini, 2010. "Direct vs Indirect Forecasts of Foreign Trade Unit Value Indices," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(2-3), pages 73-96, October.
    9. Esteban Fernández-Vázquez & Blanca Moreno, 2017. "Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator," Journal of Geographical Systems, Springer, vol. 19(4), pages 349-370, October.
    10. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    11. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    12. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
    13. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    14. Sabaj, Ernil & Kahveci, Mustafa, 2018. "Forecasting tax revenues in an emerging economy: The case of Albania," MPRA Paper 84404, University Library of Munich, Germany.

  100. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2004. "Dating Business Cycles: A Methodological Contribution with an Application to the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(4), pages 537-565, September.

    Cited by:

    1. Heather M. Anderson & Mardi Dungey & Denise R Osborn & Farshid Vahid, 2010. "Financial Integration and the Construction of Historical Financial Data for the Euro Area," Centre for Growth and Business Cycle Research Discussion Paper Series 152, Economics, The University of Manchester.
    2. Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," CREATES Research Papers 2018-01, Department of Economics and Business Economics, Aarhus University.
    3. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Poncela, Pilar, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Camacho, Maximo & Perez-Quiros, Gabriel & Saiz, Lorena, 2008. "Do European business cycles look like one?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2165-2190, July.
    6. Connor Bryant & Bernd Süssmuth, 2019. "Is the Relationship of Wealth Inequality with the Real, Financial and Housing Cycle Country-Specific?," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 47(3), pages 323-341, September.
    7. Darn , O. & Ferrara, L., 2009. "Identification of slowdowns and accelerations for the euro area economy," Working papers 239, Banque de France.
    8. Sergey V. Smirnov & Nikolai V. Kondrashov & Anna V. Petronevich, 2016. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," HSE Working papers WP BRP 122/EC/2016, National Research University Higher School of Economics.
    9. Hideaki Hirata & M. Ayhan Kose & Christopher Otrok, 2013. "Regionalization vs. globalization," Working Papers 2013-002, Federal Reserve Bank of St. Louis.
    10. Michael Funke, 2005. "Inflation in Mainland China - Modelling a Roller Coaster Ride," Quantitative Macroeconomics Working Papers 20507, Hamburg University, Department of Economics.
    11. Pamphile MEZUI-MBENG, 2012. "Cycle Du Credit Et Cycle Des Affaires Dans Les Pays De La Cemac," Cahiers du CEREFIGE 1202, CEREFIGE (Centre Europeen de Recherche en Economie Financiere et Gestion des Entreprises), Universite de Lorraine, revised 2012.
    12. Italo Colantone & Alessia Matano & Paolo Naticchioni, 2018. "New Imported Inputs, Wages and Worker Mobility," Working Papers XREAP2018-6, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2018.
    13. Danilo Leiva-Leon, 2014. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," Staff Working Papers 14-38, Bank of Canada.
    14. Cesaroni, Tatiana & Maccini, Louis & Malgarini, Marco, 2011. "Business cycle stylized facts and inventory behaviour: New evidence for the Euro area," International Journal of Production Economics, Elsevier, vol. 133(1), pages 12-24, September.
    15. Marianne Sensier & Michael Artis, 2016. "The Resilience of Employment in Wales: Through Recession and into Recovery," Regional Studies, Taylor & Francis Journals, vol. 50(4), pages 586-599, April.
    16. Roberto Casarin & Komla Mawulom Agudze & Monica Billio & Eric Girardin, 2014. "Growth-cycle phases in China�s provinces: A panel Markov-switching approach," Working Papers 2014:19, Department of Economics, University of Venice "Ca' Foscari".
    17. Tommaso Proietti, 2009. "Structural Time Series Models for Business Cycle Analysis," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 9, pages 385-433, Palgrave Macmillan.
    18. Christophe Planas & Werner Roeger & Alessandro Rossi, 2004. "How much has labour taxation contributed to European structural unemployment?," Econometrics 0408005, University Library of Munich, Germany.
    19. Marcelle Chauvet & James D. Hamilton, 2005. "Dating Business Cycle Turning Points," NBER Working Papers 11422, National Bureau of Economic Research, Inc.
    20. Tommaso Proietti, 2009. "On the Model-Based Interpretation of Filters and the Reliability of Trend-Cycle Estimates," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 186-208.
    21. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2009. "Survey Data as Coicident or Leading Indicators," Economics Working Papers ECO2009/19, European University Institute.
    22. Michael Artis & Marianne Sensier, 2011. "Tracking Unemployment in Wales through Recession and into Recovery," SERC Discussion Papers 0079, Centre for Economic Performance, LSE.
    23. Klaus Wohlrabe, 2011. "Konstruktion von Indikatoren zur Analyse der wirtschaftlichen Aktivität in den Dienstleistungsbereichen," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 55.
    24. Sonia de Lucas Santos & M. Jesús Delgado Rodríguez & Inmaculada Álvarez Ayuso & José Luis Cendejas Bueno, 2011. "Los ciclos económicos internacionales: antecedentes y revisión de la literatura," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 34(95), pages 73-84, Agosto.
    25. Yongsung Chang & Sunoong Hwang, 2011. "Asymmetric Phase Shifts in the U.S. Industrial Production Cycles," RCER Working Papers 564, University of Rochester - Center for Economic Research (RCER).
    26. Craigwell, Roland & Maurin, Alain, 2007. "A sectoral analysis of Barbados’ GDP business cycle," MPRA Paper 33428, University Library of Munich, Germany.
    27. Ritabrata Bose & Ashima Goyal, 2020. "Disaggregated Indian industrial cycles: A Spectral analysis," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2020-033, Indira Gandhi Institute of Development Research, Mumbai, India.
    28. Christian Aßmann & Jens Hogrefe & Roman Liesenfeld, 2009. "The decline in German output volatility: a Bayesian analysis," Empirical Economics, Springer, vol. 37(3), pages 653-679, December.
    29. Iolanda Lo Cascio & Stephen Pollock, 2007. "Comparative Economic Cycles," Working Papers 599, Queen Mary University of London, School of Economics and Finance.
    30. Danilo Leiva-Leon, 2017. "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers 1726, Banco de España.
    31. Guido Bulligan & Massimiliano Marcellino & Fabrizio Venditti, 2012. "Forecasting economic activity with higher frequency targeted predictors," Temi di discussione (Economic working papers) 847, Bank of Italy, Economic Research and International Relations Area.
    32. Agnieszka Gehringer & Thomas Mayer, 2021. "Measuring the Business Cycle Chronology with a Novel Business Cycle Indicator for Germany," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 71-89, April.
    33. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    34. Saulius Jokubaitis & Dmitrij Celov & Remigijus Leipus, 2019. "Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run," Papers 1906.07992, arXiv.org, revised Oct 2020.
    35. Michael Artis & Marianne Sensier, 2010. "Tracking Unemployment in the North West Through Recession and Forecasting Recovery," Centre for Growth and Business Cycle Research Discussion Paper Series 136, Economics, The University of Manchester.
    36. Periklis Gogas, 2013. "Business cycle synchronisation in the European Union: The effect of the common currency," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(1), pages 1-14.
    37. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
    38. Jefferson A. Colombo & Renan X. Cortes & Fernando I. L. Cruz & Luis H. Z. Paese, 2018. "Building State-Level Business Cycle Tracer Tools: Evidence from a Large Emerging Economy," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(5), pages 14-30, May.
    39. Santos, Sonia de Lucas & Rodríguez, María Jesús Delgado & Ayuso, Inmaculada Álvarez, 2011. "Application of factor models for the identification of countries sharing international reference-cycles," Economic Modelling, Elsevier, vol. 28(6), pages 2424-2431.
    40. Beate Schirwitz, 2009. "A comprehensive German business cycle chronology," Empirical Economics, Springer, vol. 37(2), pages 287-301, October.
    41. Beate Schirwitz, 2013. "Business Fluctuations, Job Flows and Trade Unions - Dynamics in the Economy," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 47, May.
    42. Wang, Xiaoyu & Sun, Yanlin & Peng, Bin, 2023. "Industrial linkage and clustered regional business cycles in China," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 59-72.
    43. Marianne Sensier & Michael Artis, 2016. "The Resilience of UK Regional Employment Cycles," Centre for Growth and Business Cycle Research Discussion Paper Series 229, Economics, The University of Manchester.
    44. Gogas, Periklis & Kothroulas, George, 2009. "Two speed Europe and business cycle synchronization in the European Union: The effect of the common currency," MPRA Paper 13909, University Library of Munich, Germany.
    45. Yasutomo Murasawa, 2014. "Measuring the natural rates, gaps, and deviation cycles," Empirical Economics, Springer, vol. 47(2), pages 495-522, September.
    46. Yasutomo Murasawa, 2016. "The Beveridge–Nelson decomposition of mixed-frequency series," Empirical Economics, Springer, vol. 51(4), pages 1415-1441, December.
    47. Valerie Grossman & Adrienne Mack & Enrique Martínez García, 2014. "A contribution to the chronology of turning points in global economic activity (1980-2012)," Globalization Institute Working Papers 169, Federal Reserve Bank of Dallas.
    48. Tommaso Proietti & Cecilia Frale, 2007. "New proposals for the quantification of qualitative survey data," CEIS Research Paper 98, Tor Vergata University, CEIS.
    49. Robert Lehmann & Wolfgang Nierhaus & Magnus Reif, 2016. "Eine Flash-Schätzung für die privaten Konsumausgaben in Deutschland," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(21), pages 36-41, November.
    50. Jamel Gatfaoui & Eric Girardin, 2015. "Comovement of Chinese provincial business cycles," Post-Print hal-01456105, HAL.
    51. Louise Holm, 2016. "The Swedish business cycle, 1969-2013," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(2), pages 1-22.
    52. Tom Engsted & Stig V. Møller & Magnus Sander, 2013. "Bond return predictability in expansions and recessions," CREATES Research Papers 2013-13, Department of Economics and Business Economics, Aarhus University.
    53. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    54. Siliverstovs Boriss, 2013. "Dating Business Cycles in Historical Perspective: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(5-6), pages 661-679, October.
    55. Christian Melzer & Thorsten Neumann, 2009. "Monetary policy in the euro area - has it become more powerful on the road to EMU?," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1801-1804.
    56. Maximo Camacho & Gabriel Perez-Quiros & Lorena Saiz & Universidad de Murcia, 2006. "Do european business cycles look like one $\_?$," Computing in Economics and Finance 2006 175, Society for Computational Economics.
    57. Beate Schirwitz & Christian Seiler & Klaus Wohlrabe, 2009. "Regionale Konjunkturzyklen in Deutschland – Teil II: Die Zyklendatierung," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(14), pages 24-31, July.
    58. Hasan Engin Duran & Ugo Fratesi, 2023. "Economic resilience and regionally differentiated cycles: Evidence from a turning point approach in Italy," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 219-252, April.
    59. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.
    60. Anna Solms & Bernd Süssmuth, 2022. "Business cycle characteristics of Mediterranean economies: a secular trend and cycle dynamics perspective," International Economics and Economic Policy, Springer, vol. 19(4), pages 825-862, October.
    61. Amélie Charles & Olivier Darné, 2015. "Identifying and characterizing business and acceleration cycles of French jobseekers Identifying and characterizing business and acceleration cycles of French jobseekers," Working Papers hal-01160090, HAL.
    62. Soh, Ann-Ni, 2020. "A Review on the Leading Indicator Approach towards Economic Forecasting," MPRA Paper 103854, University Library of Munich, Germany.
    63. Ferrara, L. & Vigna, O., 2009. "Cyclical relationships between GDP and housing market in France: Facts and factors at play," Working papers 268, Banque de France.
    64. Kosei Fukuda, 2008. "Differentiating between business cycles and growth cycles: evidence from 15 developed countries," Applied Economics, Taylor & Francis Journals, vol. 40(7), pages 875-883.

  101. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
    See citations under working paper version above.
  102. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2004. "Some cautions on the use of panel methods for integrated series of macroeconomic data," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 322-340, December.
    See citations under working paper version above.
  103. Jordà, Òscar & Marcellino, Massimiliano, 2003. "Modeling High-Frequency Foreign Exchange Data Dynamics," Macroeconomic Dynamics, Cambridge University Press, vol. 7(4), pages 618-635, September.

    Cited by:

    1. Helmut Herwartz, 2006. "Econometric analysis of high frequency data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 89-104, March.

  104. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    See citations under working paper version above.
  105. Massimiliano Marcellino & Grayham E. Mizon & Hans-Martin Krolzig, 2002. "A Markov-switching vector equilibrium correction model of the UK labour market," Empirical Economics, Springer, vol. 27(2), pages 233-254.
    See citations under working paper version above.
  106. Marcellino, Massimiliano & Salmon, Mark, 2002. "Robust Decision Theory And The Lucas Critique," Macroeconomic Dynamics, Cambridge University Press, vol. 6(1), pages 167-185, February.

    Cited by:

    1. Pataracchia, B., 2011. "Ambiguity and Volatility : Asset Pricing Implications," Discussion Paper 2011-042, Tilburg University, Center for Economic Research.
    2. Ekaterina Pirozhkova, 2017. "Financial frictions and robust monetary policy in the models of New Keynesian framework," BCAM Working Papers 1701, Birkbeck Centre for Applied Macroeconomics.
    3. Brock,W.A. & Durlauf,S.N. & West,K.D., 2004. "Model uncertainty and policy evaluation : some theory and empirics," Working papers 19, Wisconsin Madison - Social Systems.
    4. William A. Brock & Steven N. Durlauf, 2004. "Elements of a Theory of Design Limits to Optimal Policy," Manchester School, University of Manchester, vol. 72(s1), pages 1-18, September.
    5. Katherine Moos, 2016. "The Transvaluation of the Theory of Economic Policy: The Lucas Critique Reconsidered," Working Papers 1603, New School for Social Research, Department of Economics.
    6. Brock,W.A. & Durlauf,S.N., 2004. "Local robustness analysis : theory and application," Working papers 22, Wisconsin Madison - Social Systems.
    7. Gonzalez F. & Rodriguez A. & Gonzalez-Garcia J.R., 2005. "Uncertainty about the Persistence of Periods with Large Price Shocks and the Optimal Reaction of the Monetary Authority," Computing in Economics and Finance 2005 402, Society for Computational Economics.
    8. Kenneth Kasa, 1999. "Model uncertainty, robust policies, and the value of commitment," Working Paper Series 99-14, Federal Reserve Bank of San Francisco.
    9. Gerlach-Kristen, Petra, 2006. "Internal and external shocks in Hong Kong: Empirical evidence and policy options," Economic Modelling, Elsevier, vol. 23(1), pages 56-75, January.
    10. Rodríguez Arnulfo & González Fidel & González García Jesús R., 2007. "Uncertainty about the Persistence of Cost-Push Shocks and the Optimal Reaction of the Monetary Authority," Working Papers 2007-05, Banco de México.
    11. Brock,W.A. & Durlauf,S.N., 2004. "Macroeconomics and model uncertainty," Working papers 20, Wisconsin Madison - Social Systems.

  107. Michael Artis & Massimiliano Marcellino, 2001. "Fiscal forecasting: The track record of the IMF, OECD and EC," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 20-36.
    See citations under working paper version above.
  108. Massimiliano Marcellino & Grayham E. Mizon, 2001. "Small-system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 359-370.
    See citations under working paper version above.
  109. Marcellino, Massimiliano & Mizon, Grayham E., 2000. "Modelling shifts in the wage-price and unemployment-inflation relationships in Italy, Poland and the UK," Economic Modelling, Elsevier, vol. 17(3), pages 387-413, August.
    See citations under working paper version above.
  110. Massimiliano Marcellino, 2000. "Forecast Bias and MSFE Encompassing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(4), pages 533-542, September.

    Cited by:

    1. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    2. Michael Artis & Anindya Banerjee & Massimiliano Marcellino, "undated". "Factor forecasts for the UK," Working Papers 203, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.

  111. Federico Bonaglia & Eliana La Ferrara & Massimiliano Marcellino, 2000. "Public Capital and Economic Performance: Evidence from Italy," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 59(2), pages 221-244, September.
    See citations under working paper version above.
  112. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.

    Cited by:

    1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
    2. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    3. Antonio Diez de los Rios & Enrique Sentana, 2011. "Testing Uncovered Interest Parity: A Continuous‐Time Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(4), pages 1215-1251, November.
    4. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    5. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    6. Sebastian Rondeau, 2012. "Sources of Fluctuations in Emerging Markets: Structural Estimation with Mixed Frequency Data," 2012 Meeting Papers 1156, Society for Economic Dynamics.
    7. Noel Gaston & Gulasekaran Rajaguru, 2011. "How an Export Boom affects Unemployment," ISER Discussion Paper 0801, Institute of Social and Economic Research, The University of Osaka.
    8. Percoco, Marco, 2015. "Temporal aggregation and spatio-temporal traffic modeling," Journal of Transport Geography, Elsevier, vol. 46(C), pages 244-247.
    9. Chi-Young Choi & Nelson Mark & Donggyu Sul, 2004. "Unbiased Estimation of the Half-Life to PPP Convergence in Panel Data," NBER Working Papers 10614, National Bureau of Economic Research, Inc.
    10. Schumacher, Christian & Marcellino, Massimiliano & Foroni, Claudia, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.
    11. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The Real Effects of Financial Uncertainty Shocks: A Daily Identification Approach," Documentos de Trabajo 559, Instituto de Economia. Pontificia Universidad Católica de Chile..
    12. Tilak Abeysinghe & Anthony S. Tay, 2000. "Dynamic Regressions with Variables Observed at Different Frequencies," Econometric Society World Congress 2000 Contributed Papers 0752, Econometric Society.
    13. Jose Ignacio Lopez & Virginia Olivella, 2018. "The importance of intangible capital for the transmission of financial shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 30, pages 223-238, October.
    14. Gabriel Pons Rotger, 2000. "Temporal Aggregation and Ordinary Least Squares Estimation of Cointegrating Regressions," Econometric Society World Congress 2000 Contributed Papers 1317, Econometric Society.
    15. Markos Farag, Stephen Snudden, Greg Upton, 2024. "Can Futures Prices Predict the Real Price of Primary Commodities?," LCERPA Working Papers jc0145, Laurier Centre for Economic Research and Policy Analysis, revised 2024.
    16. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
    17. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    18. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    19. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    20. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    21. Eberhardt, Markus & Teal, Francis, 2009. "Econometrics for Grumblers: A New Look at the Literature on Cross-Country Growth Empirics," MPRA Paper 15813, University Library of Munich, Germany.
    22. J. Isaac Miller & Xi Wang, 2016. "Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 810-824, November.
    23. Maria Nikoloudaki & Dikaios Tserkezos, 2008. "Temporal Aggregation Effects in Choosing the Optimal Lag Order in Stable ARMA Models: Some Monte Carlo Results," Working Papers 0822, University of Crete, Department of Economics.
    24. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    25. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    26. Girardin, Eric & Liu, Zhenya, 2007. "The financial integration of China: New evidence on temporally aggregated data for the A-share market," China Economic Review, Elsevier, vol. 18(3), pages 354-371.
    27. Dimitra Papadovasilaki & Federico Guerrero & Rattaphon Wuthisatian & Bhraman Gulati, 2022. "The 1920s technological revolution and the crash of 1929: the role of RCA, DuPont, General Motors, and Union Carbide," SN Business & Economics, Springer, vol. 2(5), pages 1-22, May.
    28. Müller-Kademann Christian, 2015. "Internal Validation of Temporal Disaggregation: A Cloud Chamber Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(3), pages 298-319, June.
    29. J.I.Lopez & V. Olivella Moppett, 2014. "Financial Shocks and the Cyclical Behavior of Skilled and Unskilled Unemployment," Working papers 496, Banque de France.
    30. Oscar Jorda & Massimiliano Marcellino, 2003. "Stochastic Processes Subject To Time Scale Transformations: An Application To High-Frequency Fx Data," Working Papers 273, University of California, Davis, Department of Economics.
    31. Antonio Diez de los Rios, 2013. "A New Linear Estimator for Gaussian Dynamic Term Structure Models," Staff Working Papers 13-10, Bank of Canada.
    32. Grzegorz Przekota & Anna Szczepańska-Przekota, 2025. "Directions of Price Transmission on the Diesel Oil Market in Poland," Energies, MDPI, vol. 18(1), pages 1-25, January.
    33. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    34. Christian Mueller, 2006. "Testing Temporal Disaggregation," KOF Working papers 06-134, KOF Swiss Economic Institute, ETH Zurich.
    35. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    36. Hui Jun ZHANG & Jean-Marie DUFOUR & John W. GALBRAITH, 2013. "Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons," Cahiers de recherche 14-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    37. Martin Enilov, 2024. "The predictive power of commodity prices for future economic growth: Evaluating the role of economic development," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3040-3062, July.
    38. Peter Fuleky & Carl S. Bonham, 2011. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 201110, University of Hawaii at Manoa, Department of Economics.
    39. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    40. Stefan J. Hock & Sascha Raithel, 2020. "Managing Negative Celebrity Endorser Publicity: How Announcements of Firm (Non)Responses Affect Stock Returns," Management Science, INFORMS, vol. 66(3), pages 1473-1495, March.
    41. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    42. Kosei Fukuda, 2009. "Related-variables selection in temporal disaggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 343-357.
    43. Mamingi Nlandu, 2017. "Beauty and Ugliness of Aggregation over Time: A Survey," Review of Economics, De Gruyter, vol. 68(3), pages 205-227, December.
    44. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
    45. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    46. Phillip A. Cartwright & Natalija Riabko, 2019. "Do spot food commodity and oil prices predict futures prices?," Review of Quantitative Finance and Accounting, Springer, vol. 53(1), pages 153-194, July.
    47. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, April.
    48. Gulasekaran Rajaguru & Reza Siregar, 2002. "Sources of Variations Between The Inflation Rates of Korea, Thailand and Indonesia During The Post-1997 Crisis," Centre for International Economic Studies Working Papers 2002-29, University of Adelaide, Centre for International Economic Studies.
    49. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Working Paper Series 2206, European Central Bank.
    50. Mokinski, Frieder & Wölfing, Nikolas, 2013. "The effect of regulatory scrutiny asymmetric cost pass-through in power wholesale and its end," ZEW Discussion Papers 13-055, ZEW - Leibniz Centre for European Economic Research.
    51. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    52. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    53. Jin, Xing & Wang, Leping & Yu, Jun, 2007. "Temporal aggregation and risk-return relation," Finance Research Letters, Elsevier, vol. 4(2), pages 104-115, June.
    54. McCrorie, J.R. & Chambers, M.J., 2004. "Granger Causality and the Sampling of Economic Processes," Discussion Paper 2004-39, Tilburg University, Center for Economic Research.
    55. Oscar Jorda & Massimiliano Marcellino, 2003. "Time-Scale Transformations of Discrete-Time Processes," Working Papers 65, University of California, Davis, Department of Economics.
    56. J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers 1211, Department of Economics, University of Missouri.
    57. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    58. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    59. Christian, Müller, 2011. "The forward-bias puzzle: Still unsolved," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(4), pages 605-610, October.
    60. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo.
    61. Guerino Ardizzi & Simone Emiliozzi & Juri Marcucci & Libero Monteforte, 2019. "News and consumer card payments," Temi di discussione (Economic working papers) 1233, Bank of Italy, Economic Research and International Relations Area.
    62. Jesús Otero & Theodore Panagiotidis & Georgios Papapanagiotou, 2022. "Multivariate Cointegration and Temporal Aggregation: Some Further Simulation Results," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 59-70, January.
    63. Peter Vlaar & Ard den Reijer, 2004. "Forecasting inflation: An art as well as a science!," Computing in Economics and Finance 2004 148, Society for Computational Economics.
    64. Rajaguru, Gulasekaran & Abeysinghe, Tilak, 2008. "Temporal aggregation, cointegration and causality inference," Economics Letters, Elsevier, vol. 101(3), pages 223-226, December.
    65. Andrea Gazzani & Alejandro Vicondoa, 2020. "Bridge Proxy-SVAR: estimating the macroeconomic effects of shocks identified at high-frequency," Temi di discussione (Economic working papers) 1274, Bank of Italy, Economic Research and International Relations Area.
    66. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.
    67. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    68. Carlomagno, Guillermo & Fornero, Jorge & Sansone, Andrés, 2023. "A proposal for constructing and evaluating core inflation measures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(3).
    69. Guillermo Carlomagno & Jorge Fornero & Andrés Sansone, 2021. "Toward a general framework for constructing and evaluating core inflation measures," Working Papers Central Bank of Chile 913, Central Bank of Chile.
    70. Reinhard Ellwanger, Stephen Snudden, 2021. "Predictability of Aggregated Time Series," LCERPA Working Papers bm0127, Laurier Centre for Economic Research and Policy Analysis.
    71. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    72. Christian M. Hafner, 2004. "Temporal aggregation of multivariate GARCH processes," Econometric Society 2004 North American Winter Meetings 538, Econometric Society.
    73. Gulasekaran Rajaguru & Tilak Abeysinghe, 2004. "Quarterly real GDP estimates for China and ASEAN4 with a forecast evaluation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 431-447.
    74. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2016. "Structural analysis with mixed frequencies: monetary policy, uncertainty and gross capital flows," JRC Working Papers in Economics and Finance 2016-04, Joint Research Centre, European Commission.
    75. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201305, University of Hawaii at Manoa, Department of Economics.
    76. Mohammadipour, Maryam & Boylan, John E., 2012. "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models," Omega, Elsevier, vol. 40(6), pages 703-712.
    77. Tilak Abeysinghe & Gulasekaran Rajaguru, 2004. "Temporal aggregation, causality distortions and a sign rule," Econometric Society 2004 Australasian Meetings 73, Econometric Society.
    78. Xing Jin & LepingWang & JunYu, 2007. "Temporal Aggregation and Risk-Return Relation," Finance Working Papers 21917, East Asian Bureau of Economic Research.
    79. E Andreou & A Pelloni & M Sensier, 2003. "The effect of nominal shock uncertainty on output growth," Centre for Growth and Business Cycle Research Discussion Paper Series 40, Economics, The University of Manchester.
    80. Stephen M. Shellman & Brandon M. Stewart, 2007. "Political Persecution or Economic Deprivation? A Time-Series Analysis of Haitian Exodus, 1990—2004," Conflict Management and Peace Science, Peace Science Society (International), vol. 24(2), pages 121-137, April.
    81. M. Chudý & S. Karmakar & W. B. Wu, 2020. "Long-term prediction intervals of economic time series," Empirical Economics, Springer, vol. 58(1), pages 191-222, January.
    82. Massimiliano Marcellino & Grayham E. Mizon, "undated". "Small system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Working Papers 188, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    83. Bilson, Chris & Brailsford, Tim & Rajaguru, Gulasekaran, 2022. "Covered interest rate parity deviations in the Asia-Pacific," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    84. Stephen Snudden, 2024. "Don’t Ruin the Surprise: Temporal Aggregation Bias in Structural Innovations," LCERPA Working Papers jc0149, Laurier Centre for Economic Research and Policy Analysis, revised Nov 2024.
    85. Huang, Yu-Lieh, 2012. "Measuring business cycles: A temporal disaggregation model with regime switching," Economic Modelling, Elsevier, vol. 29(2), pages 283-290.
    86. Thornton, Michael A. & Chambers, Marcus J., 2017. "Continuous time ARMA processes: Discrete time representation and likelihood evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 48-65.
    87. Cartwright, Phillip A. & Riabko, Natalija, 2015. "Measuring the effect of oil prices on wheat futures prices," Research in International Business and Finance, Elsevier, vol. 33(C), pages 355-369.
    88. Michael O'Neill & Gulasekaran Rajaguru, 2020. "A response surface analysis of critical values for the lead‐lag ratio with application to high frequency and non‐synchronous financial data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3979-3990, December.
    89. Chambers, MJ & McCrorie, JR & Thornton, MA, 2017. "Continuous Time Modelling Based on an Exact Discrete Time Representation," Economics Discussion Papers 20497, University of Essex, Department of Economics.
    90. Víctor Gómez & Félix Aparicio‐Pérez, 2009. "A new state–space methodology to disaggregate multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 97-124, January.
    91. Marcellino, Massimiliano & Mizon, Grayham E., 2000. "Modelling shifts in the wage-price and unemployment-inflation relationships in Italy, Poland and the UK," Economic Modelling, Elsevier, vol. 17(3), pages 387-413, August.
    92. Rajaguru, Gulasekaran, 2004. "Impact of systematic sampling on causality in the presence of unit roots," Economics Letters, Elsevier, vol. 84(1), pages 127-132, July.
    93. Andrea Giovanni Gazzani & Alejandro Vicondoa, 2019. "Proxy-SVAR as a Bridge for Identification with Higher Frequency Data," 2019 Meeting Papers 855, Society for Economic Dynamics.
    94. Phillip A. Cartwright & Natalija Riabko, 2016. "Further evidence on the explanatory power of spot food and energy commodities market prices for futures prices," Review of Quantitative Finance and Accounting, Springer, vol. 47(3), pages 579-605, October.
    95. Massimiliano Marcellino & Grayham E. Mizon, 2000. "Wages, Prices, Productivity, Inflation and Unemployment in Italy 1970-1994," Econometric Society World Congress 2000 Contributed Papers 0911, Econometric Society.
    96. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    97. José Casals Carro & Miguel Jerez Méndez & Sonia Sotoca López, 2006. "Modelling an forecasting time series sampled at different frequencies," Documentos de Trabajo del ICAE 0603, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    98. Alfred A. Haug, 2002. "Temporal Aggregation and the Power of Cointegration Tests: a Monte Carlo Study," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 399-412, September.
    99. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    100. Stephen Nemeth & Brian Lai, 2022. "When do natural disasters lead to negotiations in a civil war?," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(1), pages 28-42, January.
    101. Bente Halvorsen & Bodil M. Larsen, 2008. "The Role of Heterogeneous Demand for Temporal and Structural Aggregation Bias," Discussion Papers 537, Statistics Norway, Research Department.
    102. Du, Yingxin & Ju, Jiandong & Ramirez, Carlos D. & Yao, Xi, 2017. "Bilateral trade and shocks in political relations: Evidence from China and some of its major trading partners, 1990–2013," Journal of International Economics, Elsevier, vol. 108(C), pages 211-225.
    103. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
    104. Jewitt, Giles & Roderick McCrorie, J., 2005. "Computing estimates of continuous time macroeconometric models on the basis of discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 397-416, April.
    105. Bartsch, Zachary, 2019. "Economic policy uncertainty and dollar-pound exchange rate return volatility," Journal of International Money and Finance, Elsevier, vol. 98(C), pages 1-1.
    106. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
    107. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2023. "Are the Effects of Uncertainty Shocks Big or Small?," Working Papers 244, Red Nacional de Investigadores en Economía (RedNIE).
    108. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print hal-00815563, HAL.
    109. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    110. Gerard J. Tellis & Philip Hans Franses, 2006. "Optimal Data Interval for Estimating Advertising Response," Marketing Science, INFORMS, vol. 25(3), pages 217-229, 05-06.
    111. Maria Elena Bontempi & Roberto Golinelli, 2012. "The effect of neglecting the slope parameters’ heterogeneity on dynamic models of corporate capital structure," Quantitative Finance, Taylor & Francis Journals, vol. 12(11), pages 1733-1751, November.
    112. Dorji, Karma Minjur Phuntsho, 2024. "Exploring Nowcasting Techniques for Real-Time GDP Estimation in Bhutan," MPRA Paper 121380, University Library of Munich, Germany, revised 30 Jun 2024.
    113. Giusto Andrea & İşcan Talan B., 2018. "The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
    114. Enrique M. Quilis, 2018. "Temporal disaggregation of economic time series: The view from the trenches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 447-470, November.
    115. Ellwanger, Reinhard & Snudden, Stephen, 2023. "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, vol. 154(C).
    116. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.
    117. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.
    118. Tierney, Heather L.R. & Kim, Jiyoon (June) & Nazarov, Zafar, 2018. "The Effects of Temporal Aggregation on Search Engine Data," MPRA Paper 84474, University Library of Munich, Germany.
    119. Marco Percoco, 2007. "Evaluating forecasting accuracy of the temporally aggregated space-time autoregressive model," Applied Economics Letters, Taylor & Francis Journals, vol. 14(9), pages 637-641.
    120. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    121. Michael A. Thornton & Marcus J. Chambers, 2013. "Temporal aggregation in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 13, pages 289-310, Edward Elgar Publishing.
    122. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).
    123. Gulasekaran Rajaguru & Michael O’Neill & Tilak Abeysinghe, 2018. "Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?," Econometrics, MDPI, vol. 6(2), pages 1-24, June.
    124. Martin McCarthy, Stephen Snudden, 2024. "Predictable by Construction: Assessing Forecast Directional Accuracy of Temporal Aggregates," LCERPA Working Papers jc0147, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    125. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    126. Müller, Christian, 2012. "A new interpretation of known facts: The case of two-way causality between trading and volatility," Economic Modelling, Elsevier, vol. 29(3), pages 664-670.
    127. Franses, Ph.H.B.F., 2016. "Yet another look at MIDAS regression," Econometric Institute Research Papers EI2016-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    128. Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023. "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers bm0142, Laurier Centre for Economic Research and Policy Analysis.
    129. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    130. Jung, Young Cheol & Das, Anupam & McFarlane, Adian, 2020. "The asymmetric relationship between the oil price and the US-Canada exchange rate," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 198-206.
    131. Mr. Tao Wu & Mr. Michele Cavallo, 2012. "Measuring Oil-Price Shocks Using Market-Based Information," IMF Working Papers 2012/019, International Monetary Fund.
    132. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    133. Chambers, MJ, 2016. "The Effects of Sampling Frequency on Detrending Methods for Unit Root Tests," Economics Discussion Papers 16062, University of Essex, Department of Economics.
    134. Uwe Hassler, 2013. "Effect of temporal aggregation on multiple time series in the frequency domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 562-573, September.

Chapters

  1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2016. "An Overview of the Factor-augmented Error-Correction Model," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 3-41, Emerald Group Publishing Limited.
    See citations under working paper version above.
  2. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Chapter 4 Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Frontiers of Economics and Globalization, in: Forecasting in the Presence of Structural Breaks and Model Uncertainty, pages 149-194, Emerald Group Publishing Limited.

    Cited by:

    1. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," BOFIT Discussion Papers 12/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    2. Fu, Zhonghao & Hong, Yongmiao & Wang, Xia, 2023. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," Journal of Econometrics, Elsevier, vol. 233(1), pages 302-331.
    3. Boniface Yemba & Yi Duan & Nabaneeta Biswas, 2023. "Government spending news and stock price index," Economics Bulletin, AccessEcon, vol. 43(4), pages 1816-1841.
    4. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    5. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    6. Urga, Giovanni & Wang, Fa, 2024. "Estimation and inference for high dimensional factor model with regime switching," Journal of Econometrics, Elsevier, vol. 241(2).
    7. Ma, Chenchen & Tu, Yundong, 2023. "Group fused Lasso for large factor models with multiple structural breaks," Journal of Econometrics, Elsevier, vol. 233(1), pages 132-154.

  3. Marcellino, Massimiliano, 2006. "Leading Indicators," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 16, pages 879-960, Elsevier.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Alessandro Girardi & Marco Ventura, 2021. "Measuring credit crunch in Italy: evidence from a survey-based indicator," Annals of Operations Research, Springer, vol. 299(1), pages 567-592, April.
    3. Heather M. Anderson & Mardi Dungey & Denise R Osborn & Farshid Vahid, 2010. "Financial Integration and the Construction of Historical Financial Data for the Euro Area," Centre for Growth and Business Cycle Research Discussion Paper Series 152, Economics, The University of Manchester.
    4. Matteo Manera & Massimiliano Serati & Michele Plotegher, 2008. "Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal," Working Papers 2008.9, Fondazione Eni Enrico Mattei.
    5. Barrera, Carlos, 2009. "Ciclos sectoriales de los negocios en el Perú e indicadores anticipados para el crecimiento del PBI no primario," Working Papers 2009-013, Banco Central de Reserva del Perú.
    6. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.
    7. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.
    8. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    9. Knut Are Aastveit & André K. Anundsen & Eyo I. Herstad, 2017. "Residential investment and recession predictability," Working Paper 2017/24, Norges Bank.
    10. van Os, Bram & van Dijk, Dick, 2024. "Accelerating peak dating in a dynamic factor Markov-switching model," International Journal of Forecasting, Elsevier, vol. 40(1), pages 313-323.
    11. Ciccarelli, Matteo & Mojon, Benoît, 2006. "Global Inflation," Kiel Working Papers 1337, Kiel Institute for the World Economy (IfW Kiel).
    12. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
    13. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    14. Travaglini, Guido, 2011. "Climate change: where is the hockey stick? evidence from millennial-scale reconstructed and updated temperature time series," MPRA Paper 35565, University Library of Munich, Germany.
    15. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    16. Owyang, Michael T. & Rapach, David E. & Wall, Howard J., 2009. "States and the business cycle," Journal of Urban Economics, Elsevier, vol. 65(2), pages 181-194, March.
    17. Pablo Galaso & Sandra Rodriguez, 2014. "A composite leading cycle indicator for Uruguay," Documentos de Trabajo (working papers) 14-09, Instituto de Economía - IECON.
    18. Máximo Camacho & Gonzalo Palmieri, 2021. "Evaluating the OECD’s main economic indicators at anticipating recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 80-93, January.

Books

  1. Artis,Michael & Banerjee,Anindya & Marcellino,Massimiliano (ed.), 2006. "The Central and Eastern European Countries and the European Union," Cambridge Books, Cambridge University Press, number 9780521849548, January.

    Cited by:

    1. Kuhar, Aleš & Erjavec, Emil & Borovšak, Katarina, 2014. "Economic trends in the Slovenian food industry during the pre - and post EUaccession period," Agroeconomia Croatica, Croatian Society of Agricultural Economists, vol. 4(01), pages 1-10, July.
    2. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
    3. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, May.
    4. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    5. E. Marrocu & R. Paci & S. Usai, 2010. "Productivity growth in the Old and New Europe: the role of agglomeration externalities," Working Paper CRENoS 201024, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    6. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    7. Mr. Daniel Leigh & Mr. Abdul d Abiad & Mr. Ashoka Mody, 2007. "International Finance and Income Convergence: Europe is Different," IMF Working Papers 2007/064, International Monetary Fund.
    8. Kelber, A., 2010. "Cohesion policy and the new Member States of the European Union," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 19, pages 77-95, Autumn.
    9. Augusto Cerqua & Roberta Di Stefano & Guido Pellegrini, 2021. "What kind of region reaps the benefits of a currency union?," Working Papers 2/21, Sapienza University of Rome, DISS.
    10. Young Patricia T, 2010. "Captured by Business? Romanian Market Governance and the New Economic Elite," Business and Politics, De Gruyter, vol. 12(1), pages 1-40, April.
    11. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    12. Chen, Zhengyang & Valcarcel, Victor J., 2021. "Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    13. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    14. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    15. Daniel Montolio & Albert Solé‐Ollé, 2009. "Road investment and regional productivity growth: the effects of vehicle intensity and congestion," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 99-118, March.
    16. Ki-Sik Hwang, 2008. "Sub-National Level Analysis on FDI Relocation towards Eastern Europe," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 11(1), pages 19-34, March.

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