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Stefano Grassi

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.

Working papers

  1. 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.

    Cited by:

    1. Gidea, Marian & Goldsmith, Daniel & Katz, Yuri & Roldan, Pablo & Shmalo, Yonah, 2020. "Topological recognition of critical transitions in time series of cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    2. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2020. "On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin," Econometrics and Statistics, Elsevier, vol. 16(C), pages 69-90.
    3. Marian Gidea & Daniel Goldsmith & Yuri Katz & Pablo Roldan & Yonah Shmalo, 2018. "Topological recognition of critical transitions in time series of cryptocurrencies," Papers 1809.00695, arXiv.org.
    4. Nicola Uras & Lodovica Marchesi & Michele Marchesi & Roberto Tonelli, 2020. "Forecasting Bitcoin closing price series using linear regression and neural networks models," Papers 2001.01127, arXiv.org.

  2. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart (L.F.) Hoogerheide & Herman (H.K.) van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Tinbergen Institute Discussion Papers 18-076/III, Tinbergen Institute.

    Cited by:

    1. van Dijk Herman K., 2024. "Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 155-176, April.
    2. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • 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.
    4. Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman & Herman van Dijk, 2019. "Partially Censored Posterior for Robust and Efficient Risk Evaluation," Tinbergen Institute Discussion Papers 19-057/III, Tinbergen Institute.
    5. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    6. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    7. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    8. 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.
    9. 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).
    10. David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021. "Loss-Based Variational Bayes Prediction," Papers 2104.14054, arXiv.org, revised May 2022.
    11. Huosong Xia & Xiaoyu Hou & Justin Zuopeng Zhang & Mohammad Zoynul Abedin, 2025. "A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 112-135, January.
    12. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2020. "Profitability of momentum strategies in Latin America," International Review of Financial Analysis, Elsevier, vol. 70(C).
    13. Roberto Casarin & Stefano Grassi & Francesco Ravazzollo & Herman K. van Dijk, 2019. "Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 19-025/III, Tinbergen Institute.
    14. Ruben Loaiza-Maya & Gael M Martin & David T. Frazier, 2020. "Focused Bayesian Prediction," Monash Econometrics and Business Statistics Working Papers 1/20, Monash University, Department of Econometrics and Business Statistics.

  3. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Predicting the Volatility of Cryptocurrency Time Series," Working Papers No 3/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

    Cited by:

    1. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2022. "Forecasting Bitcoin Spikes: A GARCH-SVM Approach," Forecasting, MDPI, vol. 4(4), pages 1-15, September.
    2. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
    3. Borri, Nicola, 2019. "Conditional tail-risk in cryptocurrency markets," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 1-19.
    4. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    5. 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.
    6. Baur, Dirk G. & Dimpfl, Thomas, 2018. "Asymmetric volatility in cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 148-151.
    7. Adebola, Solarin Sakiru & Gil-Alana, Luis A. & Madigu, Godfrey, 2019. "Gold prices and the cryptocurrencies: Evidence of convergence and cointegration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1227-1236.
    8. Cross, Jamie L. & Hou, Chenghan & Trinh, Kelly, 2021. "Returns, volatility and the cryptocurrency bubble of 2017–18," Economic Modelling, Elsevier, vol. 104(C).
    9. Wang, Weichen & An, Ran & Zhu, Ziwei, 2024. "Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective," Journal of Econometrics, Elsevier, vol. 239(2).
    10. Branimir Cvitko Cicvarić, 2020. "Volatility of Cryptocurrencies," Notitia - journal for economic, business and social issues, Notitia Ltd., vol. 1(6), pages 13-23, December.
    11. 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.

  4. Filippo Ferroni & Stefano Grassi & Miguel A. León-Ledesma, 2017. "Selecting Primal Innovations in DSGE models," Working Paper Series WP-2017-20, Federal Reserve Bank of Chicago.

    Cited by:

    1. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    2. Richard Higgins, C., 2020. "Financial frictions and changing macroeconomic volatility," Journal of Macroeconomics, Elsevier, vol. 64(C).
    3. Corbo, Vesna & Strid, Ingvar, 2020. "MAJA: A two-region DSGE model for Sweden and its main trading partners," Working Paper Series 391, Sveriges Riksbank (Central Bank of Sweden).

  5. Leopoldo Catania & Stefano Grassi, 2017. "Modelling Crypto-Currencies Financial Time-Series," CEIS Research Paper 417, Tor Vergata University, CEIS, revised 11 Dec 2017.

    Cited by:

    1. Donato Masciandaro, 2018. "Central Bank Digital Cash and Cryptocurrencies: Insights from a New Baumol–Friedman Demand for Money," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 51(4), pages 540-550, December.
    2. Emanuele Borgonovo & Stefano Caselli & Alessandra Cillo & Donato Masciandaro, 2018. "Between Cash, Deposit And Bitcoin: Would We Like A Central Bank Digital Currency? Money Demand And Experimental Economics," BAFFI CAREFIN Working Papers 1875, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    3. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    4. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    5. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    6. Gidea, Marian & Goldsmith, Daniel & Katz, Yuri & Roldan, Pablo & Shmalo, Yonah, 2020. "Topological recognition of critical transitions in time series of cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    7. Leopoldo Catania & Mads Sandholdt, 2019. "Bitcoin at High Frequency," JRFM, MDPI, vol. 12(1), pages 1-20, February.
    8. Klein, Tony & Hien, Pham Thu & Walther, Thomas, 2018. "Bitcoin Is Not the New Gold: A Comparison of Volatility, Correlation, and Portfolio Performance," QBS Working Paper Series 2018/01, Queen's University Belfast, Queen's Business School.
    9. Camilla Muglia & Luca Santabarbara & Stefano Grassi, 2019. "Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?," JRFM, MDPI, vol. 12(2), pages 1-10, May.
    10. Alessandra Cretarola & Gianna Figà-Talamanca & Marco Patacca, 2020. "Market attention and Bitcoin price modeling: theory, estimation and option pricing," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(1), pages 187-228, June.
    11. Esparcia, Carlos & Díaz, Antonio, 2024. "The football world upside down: Traditional equities as an alternative for the new fan tokens? A portfolio optimization study," Research in International Business and Finance, Elsevier, vol. 71(C).
    12. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2020. "On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin," Econometrics and Statistics, Elsevier, vol. 16(C), pages 69-90.
    13. Thomas Walther & Tony Klein, 2018. "Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach To Forecasting," Working Papers on Finance 1815, University of St. Gallen, School of Finance.
    14. Díaz, Antonio & Esparcia, Carlos & Huélamo, Diego, 2023. "Stablecoins as a tool to mitigate the downside risk of cryptocurrency portfolios," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    15. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    16. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    17. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Predicting the Volatility of Cryptocurrency Time Series," Working Papers No 3/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    18. Luisa Corrado & Tobias Schuler, 2018. "Financial Bubbles in Interbank Lending," ifo Working Paper Series 260, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    19. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    20. Tranberg, Bo & Hansen, Rasmus Thrane & Catania, Leopoldo, 2020. "Managing volumetric risk of long-term power purchase agreements," Energy Economics, Elsevier, vol. 85(C).
    21. Marian Gidea & Daniel Goldsmith & Yuri Katz & Pablo Roldan & Yonah Shmalo, 2018. "Topological recognition of critical transitions in time series of cryptocurrencies," Papers 1809.00695, arXiv.org.
    22. Shan Wu, 2021. "Co-movement and return spillover: evidence from Bitcoin and traditional assets," SN Business & Economics, Springer, vol. 1(10), pages 1-16, October.
    23. Guglielmo Maria Caporale & Luis Gil-Alana & Alex Plastun, 2017. "Persistence in the Cryptocurrency Market," Discussion Papers of DIW Berlin 1703, DIW Berlin, German Institute for Economic Research.
    24. Emanuele Borgonovo & Stefano Caselli & Alessandra Cillo & Donato Masciandaro, 2017. "Beyond Bitcoin And Cash: Do We Like A Central Bank Digital Currency? A Financial And Political Economics Approach," BAFFI CAREFIN Working Papers 1765, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    25. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).
    26. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    27. Stephanie Danielle Subramoney & Knowledge Chinhamu & Retius Chifurira, 2021. "Value at Risk estimation using GAS models with heavy tailed distributions for cryptocurrencies," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 10(4), pages 40-54, October.
    28. Jin-Bom Han & Sun-Hak Kim & Myong-Hun Jang & Kum-Sun Ri, 2020. "Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 337-353, August.
    29. Amélie Charles & Olivier Darné, 2019. "Volatility estimation for cryptocurrencies: Further evidence with jumps and structural breaks," Post-Print hal-03794543, HAL.
    30. Emanuele Borgonovo & Stefano Caselli & Alessandra Cillo & Donato Masciandaro & Giovanno Rabitti, 2018. "Cryptocurrencies, central bank digital cash, traditional money: does privacy matter?," BAFFI CAREFIN Working Papers 1895, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    31. Cross, Jamie L. & Hou, Chenghan & Trinh, Kelly, 2021. "Returns, volatility and the cryptocurrency bubble of 2017–18," Economic Modelling, Elsevier, vol. 104(C).
    32. 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.
    33. Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.

  6. 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.

    Cited by:

    1. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    2. Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2021. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," BEMPS - Bozen Economics & Management Paper Series BEMPS83, Faculty of Economics and Management at the Free University of Bozen.
    3. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    4. Monica Billio & Roberto Casarin & Enrica De Cian & Malcolm Mistry & Anthony Osuntuyi, 2020. "The impact of Climate on Economic and Financial Cycles: A Markov-switching Panel Approach," Papers 2012.14693, arXiv.org.
    5. Roberto Casarin & Giulia Mantoan & Francesco Ravazzolo, 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions," Econometrics, MDPI, vol. 4(1), pages 1-24, March.
    6. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.
    7. Leopoldo Catania, 2016. "Dynamic Adaptive Mixture Models," Papers 1603.01308, arXiv.org, revised Jan 2023.
    8. Roberto Casarin & Stefano Grassi & Francesco Ravazzollo & Herman K. van Dijk, 2019. "Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 19-025/III, Tinbergen Institute.
    9. Nalan Baştürk & Roberto Casarin & Francesco Ravazzolo & Herman K. Van Dijk, 2016. "Computational Complexity and Parallelization in Bayesian Econometric Analysis," Econometrics, MDPI, vol. 4(1), pages 1-3, February.

  7. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.

    Cited by:

    1. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Working Paper 2018/10, Norges Bank.
    2. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    3. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.

  8. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models using ParMitISEM," Tinbergen Institute Discussion Papers 16-005/III, Tinbergen Institute.

    Cited by:

    1. Jamie L. Cross & Lennart Hoogerheide & Paul Labonne & Herman K. van Dijk, 2024. "Flexible Negative Binomial Mixtures for Credible Mode Inference in Heterogeneous Count Data from Finance, Economics and Bioinformatics," Tinbergen Institute Discussion Papers 24-056/III, Tinbergen Institute.
    2. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Working Paper 2018/10, Norges Bank.
    3. 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).
    4. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.
    5. Stefano Grassi & Marco Lorusso & Francesco Ravazzolo, 2021. "Adaptive Importance Sampling for DSGE Models," BEMPS - Bozen Economics & Management Paper Series BEMPS84, Faculty of Economics and Management at the Free University of Bozen.
    6. Geweke, John & Durham, Garland, 2019. "Sequentially adaptive Bayesian learning algorithms for inference and optimization," Journal of Econometrics, Elsevier, vol. 210(1), pages 4-25.

  9. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2015. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Hohenheim Discussion Papers in Business, Economics and Social Sciences 13-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.

    Cited by:

    1. Kenda Klemen & Mladenić Dunja, 2018. "Autonomous Sensor Data Cleaning in Stream Mining Setting," Business Systems Research, Sciendo, vol. 9(2), pages 69-79, July.
    2. Barbarino, Alessandro & Bura, Efstathia, 2024. "Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables," Econometrics and Statistics, Elsevier, vol. 31(C), pages 1-18.
    3. Jeroen V.K. Rombouts & Lars Stentoft & Francesco Violante, 2017. "Variance swap payoffs, risk premia and extreme market conditions," CREATES Research Papers 2017-21, Department of Economics and Business Economics, Aarhus University.
    4. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).

  10. Baştürk, N. & Grassi, S. & Hoogerheide, L. & Opschoor, A. & van Dijk, H.K., 2015. "The R package MitISEM : efficient and robust simulation procedures for Bayesian inference," Research Memorandum 011, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Working Paper 2018/10, Norges Bank.
    2. Ivan Mendieta-Munoz & Mengheng Li, 2019. "The Multivariate Simultaneous Unobserved Compenents Model and Identification via Heteroskedasticity," Working Paper Series, Department of Economics, University of Utah 2019_06, University of Utah, Department of Economics.
    3. Dellaportas, Petros & Tsionas, Mike G., 2019. "Importance sampling from posterior distributions using copula-like approximations," Journal of Econometrics, Elsevier, vol. 210(1), pages 45-57.
    4. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.
    5. Geweke, John & Durham, Garland, 2019. "Sequentially adaptive Bayesian learning algorithms for inference and optimization," Journal of Econometrics, Elsevier, vol. 210(1), pages 4-25.

  11. Filippo Ferroni & Stefano Grassi & Miguel A. Leon-Ledesma, 2015. "Fundamental shock selection in DSGE models," Studies in Economics 1508, School of Economics, University of Kent.

    Cited by:

    1. Masaru Inaba & Kengo Nutahara & Daichi Shirai, 2022. "What drives fluctuations of labor wedge and business cycles? Evidence from Japan," CIGS Working Paper Series 22-001E, The Canon Institute for Global Studies.
    2. Den Haan, Wouter & Drechsel, Thomas, 2018. "Agnostic Structural Disturbances (ASDs): Detecting and Reducing Misspecification in Empirical Macroeconomic Models," CEPR Discussion Papers 13145, C.E.P.R. Discussion Papers.
    3. Juan Paez-Farrell, 2023. "On the Unimportance of Commitment for Monetary Policy," Working Papers 2023018, The University of Sheffield, Department of Economics.
    4. Yashar Blouri, Maximilian von Ehrlich, 2017. "On the optimal design of place-based policies: A structural evaluation of EU regional transfers," Diskussionsschriften credresearchpaper17, Universitaet Bern, Departement Volkswirtschaft - CRED.
    5. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    6. Alexander Falter & Dennis Wesselbaum, 2018. "Correlated shocks in estimated DSGE models," Economics Bulletin, AccessEcon, vol. 38(4), pages 2026-2036.
    7. Chamon Wieles & Jan Kwakkel & Willem L. Auping & Jan Willem van den End, 2024. "Scenario discovery to address deep uncertainty in monetary policy," Working Papers 818, DNB.
    8. Mario Carceller del Arco & Jan Willem van den End, 2023. "Robust monetary policy under shock uncertainty," Working Papers 793, DNB.

  12. 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. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    2. Joan Paredes & Javier J. Pérez & Gabriel Perez-Quirós, 2015. "Fiscal targets. A guide to forecasters?," Working Papers 1508, Banco de España.
    3. María Gil & Javier J. Pérez & A. Jesús Sánchez & Alberto Urtasun, 2018. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," Working Papers 1842, Banco de España.

  13. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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.
    2. Buncic, Daniel & Gisler, Katja I. M., 2015. "Global Equity Market Volatility Spillovers: A Broader Role for the United States," Economics Working Paper Series 1508, University of St. Gallen, School of Economics and Political Science.
    3. G. Cubadda & S. Grassi & B. Guardabascio, 2022. "The Time-Varying Multivariate Autoregressive Index Model," Papers 2201.07069, arXiv.org.
    4. Camba-Méndez, Gonzalo, 2020. "On the inflation risks embedded in sovereign bond yields," Working Paper Series 2423, European Central Bank.
    5. Delis, Panagiotis & Degiannakis, Stavros & Giannopoulos, Kostantinos, 2021. "What should be taken into consideration when forecasting oil implied volatility index?," MPRA Paper 110831, University Library of Munich, Germany.
    6. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.

  14. 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. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    3. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    4. Poncela Blanco, Maria Pilar, 2020. "Factor extraction using Kalman filter and smoothing: this is not just another survey," DES - Working Papers. Statistics and Econometrics. WS 30644, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Joan Paredes & Javier J. Pérez & Gabriel Perez-Quirós, 2015. "Fiscal targets. A guide to forecasters?," Working Papers 1508, Banco de España.
    6. 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.
    7. 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.
    8. 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.
    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.

  15. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Goodness C. Aye & Rangan Gupta & Shawkat Hammoudeh & Won Joong Kim, 2014. "Forecasting the Price of Gold Using Dynamic Model Averaging," Working Papers 201415, University of Pretoria, Department of Economics.
    2. Hyeyoen Kim & Doojin Ryu, 2013. "Forecasting Exchange Rate from Combination Taylor Rule Fundamental," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 81-92, September.
    3. Danglun Luo & Qianwei Ying, 2014. "Political Connections and Bank Lines of Credit," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 5-21, May.
    4. Andrea Barletta & Elisa Nicolato & Stefano Pagliarani, 2019. "The short‐time behavior of VIX‐implied volatilities in a multifactor stochastic volatility framework," Mathematical Finance, Wiley Blackwell, vol. 29(3), pages 928-966, July.
    5. Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Indirect inference with time series observed with error," CREATES Research Papers 2014-57, Department of Economics and Business Economics, Aarhus University.
    6. Naser, Hanan & Alaali, Fatema, 2015. "Can Oil Prices Help Predict US Stock Market Returns: An Evidence Using a DMA Approach," MPRA Paper 65295, University Library of Munich, Germany, revised 25 Jun 2015.
    7. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark Wohar, 2015. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Working Papers 201599, University of Pretoria, Department of Economics.
    8. Michele Costola & Matteo Iacopini & Casper Wichers, 2023. "Bayesian SAR model with stochastic volatility and multiple time-varying weights," Papers 2310.17473, arXiv.org.
    9. Rangan Gupta & Shawkat Hammoudeh & Won Joong Kim & Beatrice D. Simo-Kengne, 2013. "Forecasting China’s Foreign Exchange Reserves Using Dynamic Model Averaging: The Role of Macroeconomic Fundamentals, Financial Stress and Economic Uncertainty," Working Papers 201338, University of Pretoria, Department of Economics.
    10. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    11. Alper Gormus, N., 2016. "Do different time-horizons in volatility have any significance for the emerging markets?," Economics Letters, Elsevier, vol. 145(C), pages 29-32.
    12. Liu, Min, 2022. "The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 288-309.
    13. Kola Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2014. "Real Estate Returns Predictability Revisited: Novel Evidence from the US REITs Market," Working Papers 201454, University of Pretoria, Department of Economics.
    14. Ji‐Eun Choi & Dong Wan Shin, 2018. "Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 691-704, September.
    15. Luca Vincenzo Ballestra & Enzo D'Innocenzo & Christian Tezza, 2024. "A GARCH model with two volatility components and two driving factors," Papers 2410.14585, arXiv.org.
    16. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    17. Lo, Chien-Ling & Shih, Pai-Ta & Wang, Yaw-Huei & Yu, Min-Teh, 2019. "VIX derivatives: Valuation models and empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 1-21.
    18. Costola, Michele & Iacopini, Matteo & Wichers, Casper, 2023. "Bayesian SAR model with stochastic volatility and multiple time-varying weights," SAFE Working Paper Series 407, Leibniz Institute for Financial Research SAFE.

  16. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox," CREATES Research Papers 2013-09, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Čapek, Jan & Crespo Cuaresma, Jesús & Hauzenberger, Niko & Reichel, Vlastimil, 2023. "Macroeconomic forecasting in the euro area using predictive combinations of DSGE models," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1820-1838.
    2. Bognanni, Mark & Zito, John, 2020. "Sequential Bayesian inference for vector autoregressions with stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    3. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    4. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute, revised 04 Sep 2014.
    5. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    6. Knut Are Aastveit & Jamie Cross & Herman K. Djik, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Working Papers No 03/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    7. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    8. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    9. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    10. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Paper 2019/2, Norges Bank.
    11. 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.
    12. Roberto Casarin & Giulia Mantoan & Francesco Ravazzolo, 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions," Econometrics, MDPI, vol. 4(1), pages 1-24, March.
    13. Nima Nonejad, 2021. "Bayesian model averaging and the conditional volatility process: an application to predicting aggregate equity returns by conditioning on economic variables," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1387-1411, August.
    14. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.
    15. Yusupova, Alisa & Pavlidis, Nicos G. & Pavlidis, Efthymios G., 2023. "Dynamic linear models with adaptive discounting," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1925-1944.
    16. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    17. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    18. Roberto Casarin & Stefano Grassi & Francesco Ravazzollo & Herman K. van Dijk, 2019. "Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 19-025/III, Tinbergen Institute.
    19. Ruben Loaiza-Maya & Gael M Martin & David T. Frazier, 2020. "Focused Bayesian Prediction," Monash Econometrics and Business Statistics Working Papers 1/20, Monash University, Department of Econometrics and Business Statistics.
    20. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    21. McAlinn, Kenichiro & West, Mike, 2019. "Dynamic Bayesian predictive synthesis in time series forecasting," Journal of Econometrics, Elsevier, vol. 210(1), pages 155-169.

  17. Matt P. Dziubinski & Stefano Grassi, 2012. "Heterogeneous Computing in Economics: A Simplified Approach," CREATES Research Papers 2012-15, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    2. Bent Jesper Christensen & Morten Ørregaard Nielsen & Jie Zhu, 2012. "The impact of financial crises on the risk-return tradeoff and the leverage effect," CREATES Research Papers 2012-19, Department of Economics and Business Economics, Aarhus University.
    3. Aldrich, EM, 2014. "GPU Computing in Economics," Santa Cruz Department of Economics, Working Paper Series qt8p12748g, Department of Economics, UC Santa Cruz.
    4. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    5. Robert Kirkby, 2017. "A Toolkit for Value Function Iteration," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 1-15, January.
    6. Michael C. Hatcher & Eric M. Scheffel, 2016. "Solving the Incomplete Markets Model in Parallel Using GPU Computing and the Krusell–Smith Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 569-591, December.
    7. Baştürk, N. & Grassi, S. & Hoogerheide, L. & van Dijk, H.K., 2016. "Parallelization experience with four canonical econometric models using ParMitISEM," Research Memorandum 013, Maastricht University, Graduate School of Business and Economics (GSBE).
    8. Kaufmann, Hendrik & Kruse, Robinson & Sibbertsen, Philipp, 2012. "On tests for linearity against STAR models with deterministic trends," Hannover Economic Papers (HEP) dp-492, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    9. Oancea, Bogdan, 2014. "Parallel Computing in Economics - An Overview of the Software Frameworks," MPRA Paper 72039, University Library of Munich, Germany.
    10. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    11. John Gibson & James P Henson, 2016. "Getting the most from MATLAB: ditching canned routines and embracing coder," Economics Bulletin, AccessEcon, vol. 36(4), pages 2519-2525.

  18. Marco Nicolosi & Stefano Grassi & Elena Stanghellini, 2011. "How to measure Corporate Social Responsibility," Quaderni del Dipartimento di Economia, Finanza e Statistica 96/2011, Università di Perugia, Dipartimento Economia.

    Cited by:

    1. Becchetti, Leonardo & Solferino, Nazaria & Tessitore, Maria Elisabetta, 2013. "Corporate social responsibility and profit volatility: theory and empirical evidence," AICCON Working Papers 129-2013, Associazione Italiana per la Cultura della Cooperazione e del Non Profit.

  19. Stefano Grassi & Paolo Santucci de Magistris, 2011. "When Long Memory Meets the Kalman Filter: A Comparative Study," CREATES Research Papers 2011-14, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    2. Salman Huseynov, 2021. "Long and short memory in dynamic term structure models," CREATES Research Papers 2021-15, Department of Economics and Business Economics, Aarhus University.
    3. Tobias Hartl & Roland Weigand, 2018. "Approximate State Space Modelling of Unobserved Fractional Components," Papers 1812.09142, arXiv.org, revised May 2020.
    4. Cuestas Juan Carlos & Gil-Alana Luis Alberiko, 2016. "Testing for long memory in the presence of non-linear deterministic trends with Chebyshev polynomials," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 57-74, February.
    5. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2015. "Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach," CREATES Research Papers 2015-30, Department of Economics and Business Economics, Aarhus University.
    6. Rasmus T. Varneskov & Pierre Perron, 2017. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series WP2017-006, Boston University - Department of Economics.
    7. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
    8. Kruse, Robinson, 2015. "A modified test against spurious long memory," Economics Letters, Elsevier, vol. 135(C), pages 34-38.
    9. Dissanayake, G.S. & Peiris, M.S. & Proietti, T., 2016. "State space modeling of Gegenbauer processes with long memory," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 115-130.
    10. Fredrik N. G. Andersson & Yushu Li, 2020. "Are Central Bankers Inflation Nutters? An MCMC Estimator of the Long-Memory Parameter in a State Space Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 529-549, February.
    11. Andersson, Fredrik N. G. & Li, Yushu, 2014. "Are Central Bankers Inflation Nutters? - A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model," Discussion Papers 2014/38, Norwegian School of Economics, Department of Business and Management Science.
    12. Nima Nonejad, 2024. "Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark," Empirical Economics, Springer, vol. 67(4), pages 1497-1539, October.
    13. Andersson, Fredrik N.G. & Li, Yushu, 2013. "How Flexible are the Inflation Targets? A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model," Working Papers 2013:38, Lund University, Department of Economics.

  20. Stefano Grassi & Tommaso Proietti, 2011. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," CREATES Research Papers 2011-30, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Stefano Grassi & Miguel Leon-Ledesma & Filippo Ferroni, 2016. "Fundamental shock selection in DSGE models," 2016 Meeting Papers 47, Society for Economic Dynamics.

  21. Tommaso, Proietti & Stefano, Grassi, 2010. "Bayesian stochastic model specification search for seasonal and calendar effects," MPRA Paper 27305, University Library of Munich, Germany.

    Cited by:

    1. Stefano Grassi & Tommaso Proietti, 2011. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," CREATES Research Papers 2011-30, Department of Economics and Business Economics, Aarhus University.
    2. Stefano Grassi & Tommaso Proietti, 2010. "Characterizing economic trends by Bayesian stochastic model specification search," EERI Research Paper Series EERI_RP_2010_25, Economics and Econometrics Research Institute (EERI), Brussels.
    3. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.
    4. Rolando Gonzales Martínez, 2012. "Baysian seasonal analysis with robust priors," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 1(1), pages 88-93.

  22. Stefano Grassi & Tommaso Proietti, 2010. "Characterizing economic trends by Bayesian stochastic model specification search," EERI Research Paper Series EERI_RP_2010_25, Economics and Econometrics Research Institute (EERI), Brussels.

    Cited by:

    1. Stefano Grassi & Tommaso Proietti, 2011. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," CREATES Research Papers 2011-30, Department of Economics and Business Economics, Aarhus University.
    2. Michael O’Grady, 2019. "Estimating the Output, Inflation and Unemployment Gaps in Ireland using Bayesian Model Averaging," The Economic and Social Review, Economic and Social Studies, vol. 50(1), pages 35-76.
    3. Stefano Grassi & Miguel Leon-Ledesma & Filippo Ferroni, 2016. "Fundamental shock selection in DSGE models," 2016 Meeting Papers 47, Society for Economic Dynamics.

  23. Grassi, Stefano & Proietti, Tommaso, 2008. "Has the Volatility of U.S. Inflation Changed and How?," MPRA Paper 11453, University Library of Munich, Germany.

    Cited by:

    1. James M. Nason & Gregor W. Smith, 2013. "Measuring The Slowly Evolving Trend In Us Inflation With Professional Forecasts," Working Paper 1316, Economics Department, Queen's University.
    2. Beyer, Robert & Milivojevic, Lazar, 2021. "Dynamics and synchronization of global equilibrium interest rates," IMFS Working Paper Series 146, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    3. 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.
    4. Stefano Grassi & Nima Nonejad & Paolo Santucci De Magistris, 2017. "Forecasting With the Standardized Self‐Perturbed Kalman Filter," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 318-341, March.
    5. Eric Eisenstat & Rodney W. Strachan, 2014. "Modelling Inflation Volatility," CAMA Working Papers 2014-68, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Nonejad, Nima, 2015. "Flexible model comparison of unobserved components models using particle Gibbs with ancestor sampling," Economics Letters, Elsevier, vol. 133(C), pages 35-39.
    7. Elmar Mertens & James M Nason, 2015. "Inflation and Professional Forecast Dynamics: An Evaluation of Stickiness, Persistence, and Volatility," CAMA Working Papers 2015-06, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Hafedh Bouakez & Badye Omar Essid & Michel Normandin, 2010. "Stock Returns and Monetary Policy: Are There Any Ties ?," Cahiers de recherche 1026, CIRPEE.
    9. Jacek Kwiatkowski, 2010. "Unobserved Component Model for Forecasting Polish Inflation," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 121-129.
    10. Nonejad, Nima, 2014. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," MPRA Paper 55662, University Library of Munich, Germany.
    11. Nima Nonejad, 2013. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," CREATES Research Papers 2013-27, Department of Economics and Business Economics, Aarhus University.
    12. Nonejad Nima, 2016. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," Journal of Time Series Econometrics, De Gruyter, vol. 8(1), pages 55-90, January.
    13. Henzel, Steffen R., 2013. "Fitting survey expectations and uncertainty about trend inflation," Journal of Macroeconomics, Elsevier, vol. 35(C), pages 172-185.

Articles

  1. Filippo Ferroni & Stefano Grassi & Miguel A. León‐Ledesma, 2019. "Selecting structural innovations in DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 205-220, March.

    Cited by:

    1. Masaru Inaba & Kengo Nutahara & Daichi Shirai, 2022. "What drives fluctuations of labor wedge and business cycles? Evidence from Japan," CIGS Working Paper Series 22-001E, The Canon Institute for Global Studies.
    2. Bora Durdu & Molin Zhong, 2019. "Understanding Bank and Nonbank Credit Cycles: A Structural Exploration," Finance and Economics Discussion Series 2019-031, Board of Governors of the Federal Reserve System (U.S.).
    3. Andrew Binning, 2024. "Calculating Government Consumption Multipliers in New Zealand Using an Estimated DSGE Model," Treasury Working Paper Series 24/01, New Zealand Treasury.
    4. Damioli, Giacomo & Gregori, Wildmer Daniel, 2021. "Diplomatic relations and cross-border investments in the European Union," JRC Working Papers in Economics and Finance 2021-02, Joint Research Centre, European Commission.
    5. Berger, Tino & Everaert, Gerdie & Pozzi, Lorenzo, 2021. "Testing for international business cycles: A multilevel factor model with stochastic factor selection," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    6. Giovannini, Massimo & Pfeiffer, Philipp & Ratto, Marco, 2021. "Efficient and robust inference of models with occasionally binding constraints," JRC Working Papers in Economics and Finance 2021-03, Joint Research Centre, European Commission.
    7. Stefan Schiman, 2021. "Labor Supply Shocks and the Beveridge Curve: Empirical Evidence from EU Enlargement," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 40, pages 108-127, April.
    8. Higgins, C. Richard, 2023. "Risk and Uncertainty: The Role of Financial Frictions," Economic Modelling, Elsevier, vol. 119(C).
    9. Calo, Silvia & Gregori, Wildmer Daniel & Petracco Giudici, Marco & Rancan, Michela, 2021. "Has the Comprehensive Assessment made the European financial system more resilient?," JRC Working Papers in Economics and Finance 2021-08, Joint Research Centre, European Commission.

  2. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2018. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 107-123.
    See citations under working paper version above.
  3. Baştürk, Nalan & Grassi, Stefano & Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2017. "The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i01).
    See citations under working paper version above.
  4. Stefano Grassi & Nima Nonejad & Paolo Santucci De Magistris, 2017. "Forecasting With the Standardized Self‐Perturbed Kalman Filter," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 318-341, March.
    See citations under working paper version above.
  5. Nalan Baştürk & Stefano Grassi & Lennart Hoogerheide & Herman K. Van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM," Econometrics, MDPI, vol. 4(1), pages 1-20, March.
    See citations under working paper version above.
  6. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    See citations under working paper version above.
  7. 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.
  8. Grassi, Stefano & Santucci de Magistris, Paolo, 2015. "It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 62-78.
    See citations under working paper version above.
  9. Tommaso Proietti & Stefano Grassi, 2015. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," Empirical Economics, Springer, vol. 48(3), pages 983-1011, May.
    See citations under working paper version above.
  10. Grassi, Stefano & Santucci de Magistris, Paolo, 2014. "When long memory meets the Kalman filter: A comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 301-319.
    See citations under working paper version above.
  11. Grassi, S. & Proietti, T., 2014. "Characterising economic trends by Bayesian stochastic model specification search," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 359-374.
    See citations under working paper version above.
  12. Marco Nicolosi & Stefano Grassi & Elena Stanghellini, 2014. "Item response models to measure corporate social responsibility," Applied Financial Economics, Taylor & Francis Journals, vol. 24(22), pages 1449-1464, November.

    Cited by:

    1. Guillermo Badía & Luis Ferruz & Maria Céu Cortez, 2021. "The performance of social responsible investing from retail investors' perspective: international evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6074-6088, October.
    2. Cerqueti, Roy & Ciciretti, Rocco & Dalò, Ambrogio & Nicolosi, Marco, 2022. "A new measure of the resilience for networks of funds with applications to socially responsible investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    3. Massimo Costa & Patrizia Torrecchia, 2018. "The Concept of Value for CSR: A Debate Drawn from Italian Classical Accounting," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 25(2), pages 113-123, March.
    4. Soler-Domínguez, Amparo & Matallín-Sáez, Juan Carlos, 2016. "Socially (ir)responsible investing? The performance of the VICEX Fund from a business cycle perspective," Finance Research Letters, Elsevier, vol. 16(C), pages 190-195.
    5. Roy Cerqueti & Rocco Ciciretti & Ambrogio Dalò & Marco Nicolosi, 2022. "Mitigating Contagion Risk by ESG Investing," Sustainability, MDPI, vol. 14(7), pages 1-13, March.
    6. Francisco González Santa Cruz & Iliana Loor Alcívar & Nelly Moreira Mero & Amalia Hidalgo-Fernández, 2020. "Analysis of the Dimensions of Corporate Social Responsibility: Study Applied to Co-operativism in Ecuador," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(2), pages 517-534, April.
    7. Pornanong Budsaratragoon & Boonlert Jitmaneeroj, 2021. "Corporate Sustainability and Stock Value in Asian–Pacific Emerging Markets: Synergies or Tradeoffs among ESG Factors?," Sustainability, MDPI, vol. 13(11), pages 1-25, June.

  13. Matt Dziubinski & Stefano Grassi, 2014. "Heterogeneous Computing in Economics: A Simplified Approach," Computational Economics, Springer;Society for Computational Economics, vol. 43(4), pages 485-495, April.
    See citations under working paper version above.
  14. Grassi Stefano & Proietti Tommaso, 2010. "Has the Volatility of U.S. Inflation Changed and How?," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-22, September.
    See citations under working paper version above.
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