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Joshua C.C. Chan

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Joshua Chan & Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2011. "Time Varying Dimension Models," Working Papers 1116, University of Strathclyde Business School, Department of Economics.

    Mentioned in:

    1. Do not waste degrees of freedom with macro data
      by Economic Logician in Economic Logic on 2011-06-30 19:21:00

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Joshua C. C. Chan, 2005. "Replication of the results in 'learning about heterogeneity in returns to schooling'," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 439-443.

    Mentioned in:

    1. Replication of the results in 'learning about heterogeneity in returns to schooling' (Journal of Applied Econometrics 2005) in ReplicationWiki ()
  2. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.

    Mentioned in:

    1. A New Model of Trend Inflation (J Business & Econ Statistics 2013) in ReplicationWiki ()

Working papers

  1. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.

    Cited by:

    1. Eraslan, Sercan & Reif, Magnus, 2023. "A latent weekly GDP indicator for Germany," Technical Papers 08/2023, Deutsche Bundesbank.
    2. 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).
    3. 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).
    4. 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.
    5. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    6. Luca Barbaglia & Lorenzo Frattarolo & Niko Hauzenberger & Dominik Hirschbuehl & Florian Huber & Luca Onorante & Michael Pfarrhofer & Luca Tiozzo Pezzoli, 2024. "Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model," Papers 2401.10054, arXiv.org.

  2. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.

    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. Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024. "What drives the European carbon market? Macroeconomic factors and forecasts," Working Papers 2024.02, Fondazione Eni Enrico Mattei.
    3. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    4. Milas, Costas & Panagiotidis, Theodore & Papapanagiotou, Georgios, 2024. "UK Foreign Direct Investment in uncertain economic times," Journal of International Money and Finance, Elsevier, vol. 147(C).
    5. 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.

  3. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.

    Cited by:

    1. Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
    2. 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.
    3. Chaya Weerasinghe & Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier, 2023. "ABC-based Forecasting in State Space Models," Monash Econometrics and Business Statistics Working Papers 12/23, Monash University, Department of Econometrics and Business Statistics.
    4. 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).
    5. David T. Frazier & Gael M. Martin & Ruben Loaiza-Maya, 2022. "Variational Bayes in State Space Models: Inferential and Predictive Accuracy," Monash Econometrics and Business Statistics Working Papers 1/22, Monash University, Department of Econometrics and Business Statistics.
    6. Mauro Bernardi & Daniele Bianchi & Nicolas Bianco, 2022. "Smoothing volatility targeting," Papers 2212.07288, arXiv.org.
    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. Flavio Pérez Rojo & Gabriel Rodríguez, 2023. "Jane Haldimand Marcet: Impact of Monetary Policy Shocks in the Peruvian Economy Over Time," Documentos de Trabajo / Working Papers 2023-523, Departamento de Economía - Pontificia Universidad Católica del Perú.
    9. 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.
    10. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    11. Gael M. Martin & David T. Frazier & Christian P. Robert, 2021. "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers 24/21, Monash University, Department of Econometrics and Business Statistics.
    12. 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.
    13. Ter Steege, Lucas, 2024. "Variational inference for Bayesian panel VAR models," Working Paper Series 2991, European Central Bank.
    14. Mauro Bernardi & Daniele Bianchi & Nicolas Bianco, 2022. "Variational inference for large Bayesian vector autoregressions," Papers 2202.12644, arXiv.org, revised Jun 2023.
    15. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Efficient variational approximations for state space models," Papers 2210.11010, arXiv.org, revised Jun 2023.
    16. Lin Deng & Michael Stanley Smith & Worapree Maneesoonthorn, 2023. "Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns," Papers 2308.05564, arXiv.org, revised Jul 2024.

  4. Joshua C. C. Chan, 2022. "Large Hybrid Time-Varying Parameter VARs," Papers 2201.07303, arXiv.org, revised Jun 2022.

    Cited by:

    1. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    2. 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.
    3. Milas, Costas & Panagiotidis, Theodore & Papapanagiotou, Georgios, 2024. "UK Foreign Direct Investment in uncertain economic times," Journal of International Money and Finance, Elsevier, vol. 147(C).
    4. 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.
    5. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    6. Zhou, Dong-hai & Liu, Xiao-xing, 2023. "Do world stock markets “jump” together? A measure of high-frequency volatility risk spillover networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    7. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Efficient variational approximations for state space models," Papers 2210.11010, arXiv.org, revised Jun 2023.
    8. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    9. 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).
    10. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  5. Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, arXiv.org.

    Cited by:

    1. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Bayesian Modeling of Time-Varying Parameters Using Regression Trees," Working Papers 23-05, Federal Reserve Bank of Cleveland.
    2. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2022. "Bayesian Modeling of TVP-VARs Using Regression Trees," Papers 2209.11970, arXiv.org, revised May 2023.
    3. Massimiliano MARCELLINO & Michael PFARRHOFER, 2024. "Bayesian nonparametric methods for macroeconomic forecasting," BAFFI CAREFIN Working Papers 24224, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    4. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.

  6. Joshua C. C. Chan & Gary Koop & Xuewen Yu, 2021. "Large Order-Invariant Bayesian VARs with Stochastic Volatility," Papers 2111.07225, arXiv.org.

    Cited by:

    1. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    2. Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, 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. Wu, Ping & Koop, Gary, 2023. "Estimating the ordering of variables in a VAR using a Plackett–Luce prior," Economics Letters, Elsevier, vol. 230(C).
    6. Braun, Robin, 2021. "The importance of supply and demand for oil prices: evidence from non-Gaussianity," Bank of England working papers 957, Bank of England.
    7. Uddin, Mohammad Jalal, 2023. "Investigating the impulse responses of renewable energy in the context of China: A Bayesian VAR Approach," Renewable Energy, Elsevier, vol. 219(P2).
    8. Helmut Lutkepohl & Fei Shang & Luis Uzeda & Tomasz Wo'zniak, 2024. "Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference," Papers 2404.11057, arXiv.org.
    9. Florian Huber & Gary Koop & Massimiliano Marcellino & Tobias Scheckel, 2024. "Bayesian modelling of VAR precision matrices using stochastic block networks," Papers 2407.16349, arXiv.org.
    10. 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.
    11. Florian Huber & Gary Koop, 2023. "Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks," Papers 2305.16827, arXiv.org.
    12. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2021. "Vector autoregression models with skewness and heavy tails," Working Papers 2021:8, Örebro University, School of Business.
    13. Jonas E. Arias & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models," Working Papers 21-21, Federal Reserve Bank of Philadelphia.
    14. 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.
    15. 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.
    16. Ping Wu & Gary Koop, 2022. "Fast, Order-Invariant Bayesian Inference in VARs using the Eigendecomposition of the Error Covariance Matrix," Working Papers 2310, University of Strathclyde Business School, Department of Economics.
    17. Luis Gruber & Gregor Kastner, 2022. "Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!," Papers 2206.04902, arXiv.org, revised Nov 2024.
    18. Prüser, Jan & Blagov, Boris, 2022. "Improving inference and forecasting in VAR models using cross-sectional information," Ruhr Economic Papers 960, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  7. Joshua C. C. Chan, 2021. "Asymmetric Conjugate Priors for Large Bayesian VARs," Papers 2111.07170, arXiv.org.

    Cited by:

    1. Leonardo Nogueira Ferreira & Silvia Miranda-Agrippino & Giovanni Ricco, 2023. "Bayesian Local Projections," Working Papers Series 581, Central Bank of Brazil, Research Department.
    2. Florian Huber & Gary Koop, 2021. "Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions," Papers 2107.07804, arXiv.org.
    3. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    4. Minsu Chang & Frank Schorfheide, 2024. "On the Effects of Monetary Policy Shocks on Income and Consumption Heterogeneity," PIER Working Paper Archive 24-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    6. Giovanni Ricco & Riccardo Degasperi & Seokki S. Hong, 2020. "The Global Transmission of U.S. Monetary Policy," Working Papers 814, Economic Research Southern Africa.
    7. 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.
    8. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon & Ping Wu, 2023. "Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting," Working Papers 2311, University of Strathclyde Business School, Department of Economics.
    9. Zhang, Wen, 2022. "China’s government spending and global inflation dynamics: The role of the oil price channel," Energy Economics, Elsevier, vol. 110(C).
    10. 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.
    11. 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.
    12. Flavio Pérez Rojo & Gabriel Rodríguez, 2023. "Jane Haldimand Marcet: Impact of Monetary Policy Shocks in the Peruvian Economy Over Time," Documentos de Trabajo / Working Papers 2023-523, Departamento de Economía - Pontificia Universidad Católica del Perú.
    13. 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.
    14. Andrea Carriero & Davide Pettenuzzo & Shubhranshu Shekhar, 2024. "Macroeconomic Forecasting with Large Language Models," Papers 2407.00890, arXiv.org.
    15. Tony Chernis & Gary Koop & Emily Tallman & Mike West, 2024. "Decision synthesis in monetary policy," Papers 2406.03321, arXiv.org.
    16. Massimiliano Marcellino & Andrea Renzetti & Tommaso Tornese, 2024. "Nowcasting distributions: a functional MIDAS model," Papers 2411.05629, arXiv.org.
    17. Loria, Francesca & Matthes, Christian & Wang, Mu-Chun, 2022. "Economic theories and macroeconomic reality," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 105-117.
    18. Massimiliano Marcellino & Andrea Renzetti & Tommaso Tornese, 2024. "Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model," Papers 2411.05695, arXiv.org.
    19. Joshua C. C. Chan & Davide Pettenuzzo & Aubrey Poon & Dan Zhu, 2024. "Conditional Forecasts in Large Bayesian VARs with Multiple Equality and Inequality Constraints," Papers 2407.02262, arXiv.org.

  8. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2021. "Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach," Papers 2112.11315, arXiv.org.

    Cited by:

    1. Pettenuzzo, Davide & Sabbatucci, Riccardo & Timmermann, Allan, 2023. "Dividend suspensions and cash flows during the Covid-19 pandemic: A dynamic econometric model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1522-1541.
    2. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.

  9. Joshua C.C. Chan & Rodney W. Strachan, 2020. "Bayesian state space models in macroeconometrics," CAMA Working Papers 2020-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Felix Chan & Les Oxley, 2023. "A pulse check on recent developments in time series econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 3-6, February.
    2. Srdelic, Leonarda & Davila-Fernandez, Marwil J., 2022. "Demographic transition and economic growth in 6-EU member states," MPRA Paper 112188, University Library of Munich, Germany.
    3. Giacomo Rella, 2021. "The Fed, housing and household debt over time," Department of Economics University of Siena 850, Department of Economics, University of Siena.
    4. 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.
    5. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    6. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    7. 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.
    8. Jamie L. Cross & Lennart Hoogerheide & Paul Labonne & Herman K. van Dijk, 2023. "Bayesian Mode Inference for Discrete Distributions in Economics and Finance," Working Papers No 11/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

  10. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2019. "An automated prior robustness analysis in Bayesian model comparison," CAMA Working Papers 2019-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. 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.
    2. 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.

  11. Joshua C. C. Chan, 2019. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," CAMA Working Papers 2019-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
    2. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    3. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2021. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Papers 2112.01995, arXiv.org, revised Nov 2022.
    4. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    5. 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.
    6. 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.
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Working Papers 22-25, Federal Reserve Bank of Cleveland.
    8. 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.
    9. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic Forecasting in a Multi-country Context," Working Papers 22-02, Federal Reserve Bank of Cleveland.
    10. 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.
    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. Wu, Ping & Koop, Gary, 2023. "Estimating the ordering of variables in a VAR using a Plackett–Luce prior," Economics Letters, Elsevier, vol. 230(C).
    13. Milas, Costas & Panagiotidis, Theodore & Papapanagiotou, Georgios, 2024. "UK Foreign Direct Investment in uncertain economic times," Journal of International Money and Finance, Elsevier, vol. 147(C).
    14. Nonejad, Nima, 2023. "Modeling the out-of-sample predictive relationship between equity premium, returns on the price of crude oil and economic policy uncertainty using multivariate time-varying dimension models," Energy Economics, Elsevier, vol. 126(C).
    15. 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.
    16. Hauber, Philipp, 2022. "Real-time nowcasting with sparse factor models," EconStor Preprints 251551, ZBW - Leibniz Information Centre for Economics.
    17. 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.
    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.
    19. Luis Gruber & Gregor Kastner, 2022. "Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!," Papers 2206.04902, arXiv.org, revised Nov 2024.
    20. Prüser, Jan & Blagov, Boris, 2022. "Improving inference and forecasting in VAR models using cross-sectional information," Ruhr Economic Papers 960, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  12. 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.

    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. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    3. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Paper 2023/9, Norges Bank.
    4. Florian Eckert & Nina Mühlebach, 2021. "Global and Local Components of Output Gaps," KOF Working papers 21-497, KOF Swiss Economic Institute, ETH Zurich.
    5. 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.
    6. 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.
    7. Iseringhausen, Martin & Petrella, Ivan & Theodoridis, Konstantinos, 2021. "Aggregate Skewness and the Business Cycle," Cardiff Economics Working Papers E2021/30, Cardiff University, Cardiff Business School, Economics Section.
    8. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
    9. 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.
    10. 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.
    11. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    12. Thomas St rdal Gundersen & Even Soltvedt Hvinden, 2021. "OPEC's crude game: Strategic Competition and Regime-switching in Global Oil Markets," Working Papers No 01/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    13. Andrea Renzetti, 2023. "Modelling and Forecasting Macroeconomic Risk with Time Varying Skewness Stochastic Volatility Models," Papers 2306.09287, arXiv.org, revised Nov 2023.
    14. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    15. Rick Bohte & Luca Rossini, 2019. "Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models," JRFM, MDPI, vol. 12(3), pages 1-18, September.
    16. 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.
    17. Nadiia Shapovalenko, 2021. "A BVAR Model for Forecasting Ukrainian Inflation and GDP," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 251, pages 14-36.
    18. Florian Huber & Gary Koop, 2023. "Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks," Papers 2305.16827, arXiv.org.
    19. Michael P. Clements & Ana Beatriz Galvão, 2023. "Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 164-185, March.
    20. Haroon Mumtaz & Fulvia Marotta, 2023. "Vulnerability to Climate Change: Evidence from a Dynamic Factor Model," Working Papers 961, Queen Mary University of London, School of Economics and Finance.
    21. Luis Gruber & Gregor Kastner, 2022. "Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!," Papers 2206.04902, arXiv.org, revised Nov 2024.

  13. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2019. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," CAMA Working Papers 2019-46, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    2. 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.
    3. 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.
    4. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    5. Massimiliano Marcellino & Andrea Renzetti & Tommaso Tornese, 2024. "Nowcasting distributions: a functional MIDAS model," Papers 2411.05629, arXiv.org.
    6. Nadiia Shapovalenko, 2021. "A BVAR Model for Forecasting Ukrainian Inflation and GDP," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 251, pages 14-36.
    7. 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.
    8. 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.

  14. Joshua C.C. Chan & Liana Jacobi & Dan Zhu, 2018. "How sensitive are VAR forecasts to prior hyperparameters? An automated sensitivity analysis," CAMA Working Papers 2018-25, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. 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.
    2. 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.
    3. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    4. Zhongdong Yu & Wei Liu & Liming Chen & Serkan Eti & Hasan Dinçer & Serhat Yüksel, 2019. "The Effects of Electricity Production on Industrial Development and Sustainable Economic Growth: A VAR Analysis for BRICS Countries," Sustainability, MDPI, vol. 11(21), pages 1-13, October.
    5. 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.

  15. Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate stochastic volatility with co-heteroscedasticity," CAMA Working Papers 2018-52, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. 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.
    2. Sergey Sinelnikov-Murylev & Alexandr Radygin (ed.), 2018. "Russian Economy in 2017. Trends and Outlooks. In Russian," Books, Gaidar Institute for Economic Policy, edition 1, volume 39, number re39-2017-ru.
    3. Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," Working Paper series 23-11, Rimini Centre for Economic Analysis.
    4. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    5. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    6. Jonas E. Arias & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models," Working Papers 21-21, Federal Reserve Bank of Philadelphia.

  16. Joshua Chan & Luca Benati & Eric Eisenstat & Gary Koop, 2018. "Identifying Noise Shocks," Working Paper Series 41, Economics Discipline Group, UTS Business School, University of Technology, Sydney.

    Cited by:

    1. Luca Benati & Thomas A. Lubik, 2021. "Searching for Hysteresis," Working Paper 21-03, Federal Reserve Bank of Richmond.
    2. Luca Gambetti & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2018. "The Effect of News Shocks and Monetary Policy," Working Paper series 18-19, Rimini Centre for Economic Analysis.
    3. Ansgar Belke & Steffen Elstner & Svetlana Rujin, 2022. "Growth Prospects and the Trade Balance in Advanced Economies," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1209-1234, October.

  17. 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.

    Cited by:

    1. Jim Hart & Bernardino D'Amico & Francesco Pomponi, 2021. "Whole‐life embodied carbon in multistory buildings: Steel, concrete and timber structures," Journal of Industrial Ecology, Yale University, vol. 25(2), pages 403-418, April.
    2. 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.
    3. Simon Beyeler, 2019. "Streamlining Time-varying VAR with a Factor Structure in the Parameters," Working Papers 19.03, Swiss National Bank, Study Center Gerzensee.
    4. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    5. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    6. 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).

  18. 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.

    Cited by:

    1. Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
    2. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    3. Yuliya Rychalovska & Sergey Slobodyan & Rafael Wouters, 2023. "Professional Survey Forecasts and Expectations in DSGE Models," CERGE-EI Working Papers wp766, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    4. Laumer, Sebastian, 2020. "Government spending and heterogeneous consumption dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    5. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    6. 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).
    7. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian vector autoregressions," LSE Research Online Documents on Economics 87393, London School of Economics and Political Science, LSE Library.
    8. Canova, Fabio & Matthes, Christian, 2018. "A composite likelihood approach for dynamic structural models," CEPR Discussion Papers 13245, C.E.P.R. Discussion Papers.
    9. Raanju R. Sundararajan & Wagner Barreto‐Souza, 2023. "Student‐t stochastic volatility model with composite likelihood EM‐algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 125-147, January.
    10. 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.
    11. 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.
    12. Gunawan, David & Kohn, Robert & Nott, David, 2021. "Variational Bayes approximation of factor stochastic volatility models," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1355-1375.
    13. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.

  19. 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.

    Cited by:

    1. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting," Papers 2404.02671, arXiv.org, revised Nov 2024.
    2. William Chen & Marco Del Negro & Michele Lenza & Giorgio E. Primiceri & Andrea Tambalotti, 2020. "What’s Up with the Phillips Curve?," Liberty Street Economics 20200918a, Federal Reserve Bank of New York.
    3. Bo Zhang & Jamie Cross & Na Guo, 2020. "Time-Varying Trend Models for Forecasting Inflation in Australia," Working Papers No 09/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    5. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    6. Arango-Castillo, Lenin & Orraca, María José & Molina, G. Stefano, 2023. "The global component of headline and core inflation in emerging market economies and its ability to improve forecasting performance," Economic Modelling, Elsevier, vol. 120(C).
    7. 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.
    8. 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.
    9. Jihyun Park & Andrey Sarantsev, 2024. "The VIX as Stochastic Volatility for Corporate Bonds," Papers 2410.22498, arXiv.org, revised Nov 2024.
    10. 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.
    11. Na Guo & Bo Zhang & Jamie L. Cross, 2022. "Time‐varying trend models for forecasting inflation in Australia," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 316-330, March.
    12. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.

  20. Joshua C.C. Chan & Eric Eisenstat, 2018. "Comparing hybrid time-varying parameter VARs," CAMA Working Papers 2018-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Helge Berger & Sune Karlsson & Pär Österholm, 2023. "A note of caution on the relation between money growth and inflation," Scottish Journal of Political Economy, Scottish Economic Society, vol. 70(5), pages 479-496, November.
    2. 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.
    3. 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).
    4. Jiménez, Alvaro & Rodríguez, Gabriel & Ataurima Arellano, Miguel, 2023. "Time-varying impact of fiscal shocks over GDP growth in Peru: An empirical application using hybrid TVP-VAR-SV models," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 314-332.
    5. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    6. 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.
    7. 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.
    8. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.

  21. Joshua C C Chan & Yong Song, 2017. "Measuring inflation expectations uncertainty using high-frequency data," CAMA Working Papers 2017-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    2. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    3. Miescu, Mirela S., 2023. "Uncertainty shocks in emerging economies: A global to local approach for identification," European Economic Review, Elsevier, vol. 154(C).
    4. 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.
    5. Ryngaert, Jane M., 2022. "Inflation disasters and consumption," Journal of Monetary Economics, Elsevier, vol. 129(S), pages 67-81.
    6. 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).
    7. Ren, Yi-Shuai & Klein, Tony & Jiang, Yong & Ma, Chao-Qun & Yang, Xiao-Guang, 2024. "Dynamic spillovers among global oil shocks, economic policy uncertainty, and inflation expectation uncertainty under extreme shocks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    8. Carola Binder & Wesley Janson & Randal J. Verbrugge, 2019. "Thinking Outside the Box: Do SPF Respondents Have Anchored Inflation Expectations?," Working Papers 19-15, Federal Reserve Bank of Cleveland.

  22. Joshua C C Chan & Angelia L Grant, 2017. "Measuring the output gap using stochastic model specification search," CAMA Working Papers 2017-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. 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.
    2. James Morley & Benjamin Wong, 2017. "Estimating and accounting for the output gap with large Bayesian vector autoregressions," CAMA Working Papers 2017-46, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Alberto Montagnoli & Konstantinos Mouratidis & Kemar Whyte, 2018. "Assessing the Cyclical Behaviour of Bank Capital Buyers in a Finance-Augmented Macro-Economy," Working Papers 2018003, The University of Sheffield, Department of Economics.
    4. Fu, Bowen, 2020. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models," Economic Modelling, Elsevier, vol. 88(C), pages 320-340.
    5. Manuel González-Astudillo & John M. Roberts, 2022. "When are trend–cycle decompositions of GDP reliable?," Empirical Economics, Springer, vol. 62(5), pages 2417-2460, May.
    6. Nataliia Ostapenko, 2022. "Do output gap estimates improve inflation forecasts in Slovakia?," Working and Discussion Papers WP 4/2022, Research Department, National Bank of Slovakia.

  23. 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.

    Cited by:

    1. 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.
    2. Lang, Jan Hannes & Welz, Peter, 2018. "Semi-structural credit gap estimation," Working Paper Series 2194, European Central Bank.
    3. Kabundi, Alain & Poon, Aubrey & Wu, Ping, 2023. "A time-varying Phillips curve with global factors: Are global factors important?," Economic Modelling, Elsevier, vol. 126(C).
    4. Canova, Fabio & Ferroni, Filippo, 2020. "A hitchhiker guide to empirical macro models," CEPR Discussion Papers 15446, C.E.P.R. Discussion Papers.
    5. Fernandes, Mário Correia & Dutra, Tiago Mota & Dias, José Carlos & Teixeira, João C.A., 2023. "Modelling output gaps in the Euro Area with structural breaks: The COVID-19 recession," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1046-1058.
    6. Xu, Jia & Tan, Xiujie & He, Gang & Liu, Yu, 2019. "Disentangling the drivers of carbon prices in China's ETS pilots — An EEMD approach," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 1-9.
    7. Ahn, Hie Joo, 2023. "Duration structure of unemployment hazards and the trend unemployment rate," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    8. 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.
    9. Buncic, Daniel, 2020. "Econometric issues with Laubach and Williams’ estimates of the natural rate of interest," Working Paper Series 397, Sveriges Riksbank (Central Bank of Sweden).
    10. Cláudia Duarte & José R. Maria & Sharmin Sazedj, 2019. "Trends and cycles under changing economic conditions," Working Papers w201918, Banco de Portugal, Economics and Research Department.
    11. 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).
    12. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    13. Jung, Yong-Gook & Kim, Jinyong, 2023. "Banks’ net interest rate spread and the transmission of monetary policy in Korea," Journal of Asian Economics, Elsevier, vol. 89(C).
    14. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    15. Nicolò Maffei-Faccioli, 2021. "Identifying the sources of the slowdown in growth: Demand vs. supply," Working Paper 2021/9, Norges Bank.
    16. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    17. 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.
    18. Gabor Katay & Lisa Kerdelhué & Matthieu Lequien, 2020. "Semi-Structural VAR and Unobserved Components Models to Estimate Finance-Neutral Output Gap," JRC Working Papers in Economics and Finance 2020-11, Joint Research Centre, European Commission.
    19. Gómez Julián, José Mauricio, 2024. "Sectorial Exclusion Criteria in the Marxist Analysis of the Average Rate of Profit: The United States Case (1960-2020)," OSF Preprints seqbf, Center for Open Science.
    20. Luis Eduardo Castillo & David Florián Hoyle, 2019. "Measuring the output gap, potential output growth and natural interest rate from a semi-structural dynamic model for Peru," Working Papers 159, Peruvian Economic Association.
    21. Agovino, Massimiliano & Musella, Gaetano & Scaletti, Alessandro, 2022. "Equilibrium and efficiency in the first aid services market: The case of the emergency department of Sorrento," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    22. Martin Boďa & Mariana Považanová, 2023. "How credible are Okun coefficients? The gap version of Okun’s law for G7 economies," Economic Change and Restructuring, Springer, vol. 56(3), pages 1467-1514, June.
    23. Nicolo Maffei-Faccioli, 2020. "Identifying the Sources of the Slowdown in Growth: Demand vs. Supply," 2020 Papers pma2978, Job Market Papers.
    24. Massimiliano Agovino & Michele Bevilacqua & Massimiliano Cerciello, 2022. "Language as a proxy for cultural change. A contrastive analysis for French and Italian lexicon on male homosexuality," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(1), pages 149-172, February.
    25. Yahya, Farzan & Lee, Chien-Chiang, 2023. "Disentangling the asymmetric effect of financialization on the green output gap," Energy Economics, Elsevier, vol. 125(C).
    26. Richard K. Crump & Nikolay Gospodinov & Hunter Wieman, 2023. "Sparse Trend Estimation," Staff Reports 1049, Federal Reserve Bank of New York.
    27. Kristian Jönsson, 2018. "Extending the state-space representation of the judgement-augmented Hodrick-Prescott filter," Economics Bulletin, AccessEcon, vol. 38(1), pages 623-628.
    28. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  24. Joshua C.C. Chan & Angelia L. Grant, 2015. "Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean," CAMA Working Papers 2015-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Benjamin K. Johannsen & Elmar Mertens, 2016. "A Time Series Model of Interest Rates With the Effective Lower Bound," Finance and Economics Discussion Series 2016-033, Board of Governors of the Federal Reserve System (U.S.).
    2. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    3. Jamie L. Cross & Chenghan Hou & Bao H. Nguyen, 2018. "On the China factor in international oil markets: A regime switching approach," Working Papers No 11/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2014. "Large Bayesian VARMAs," SIRE Discussion Papers 2015-06, Scottish Institute for Research in Economics (SIRE).
    5. Doğan, Osman, 2023. "Modified harmonic mean method for spatial autoregressive models," Economics Letters, Elsevier, vol. 223(C).
    6. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    7. Siddhartha Chib & Minchul Shin & Fei Tan, 2020. "High-Dimensional DSGE Models: Pointers on Prior, Estimation, Comparison, and Prediction∗," Working Papers 20-35, Federal Reserve Bank of Philadelphia.
    8. 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.
    9. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Pacifico, Antonio, 2020. "Structural Panel Bayesian VAR with Multivariate Time-varying Volatility to jointly deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," MPRA Paper 104292, University Library of Munich, Germany.
    11. 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.
    12. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    13. Stylianos Asimakopoulos & Marco Lorusso & Francesco Ravazzolo, 2023. "A Bayesian DSGE Approach to Modelling Cryptocurrency," Working Papers No 09/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    15. Diegel, Max, 2022. "Time-varying credibility, anchoring and the Fed's inflation target," Discussion Papers 2022/9, Free University Berlin, School of Business & Economics.
    16. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Siddhartha Chib & Minchul Shin & Fei Tan, 2021. "DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors," Working Papers 21-02, Federal Reserve Bank of Philadelphia.
    18. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    19. 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.

  25. Joshua C.C. Chan & Angelia L. Grant, 2015. "A Bayesian model comparison for trend-cycle decompositions of output," CAMA Working Papers 2015-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. 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.
    2. Melolinna, Marko & Tóth, Máté, 2019. "Trend and cycle shocks in Bayesian unobserved components models for UK productivity," Bank of England working papers 826, Bank of England.
    3. Güneş Kamber & James Morley & Benjamin Wong, 2017. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," Reserve Bank of New Zealand Discussion Paper Series DP2017/01, Reserve Bank of New Zealand.
    4. Marko Melolinna & Máté Tóth, 2019. "Output gaps, inflation and financial cycles in the UK," Empirical Economics, Springer, vol. 56(3), pages 1039-1070, March.
    5. Jaeho Kim & Sora Chon, 2020. "Why are Bayesian trend-cycle decompositions of US real GDP so different?," Empirical Economics, Springer, vol. 58(3), pages 1339-1354, March.
    6. Bofinger, Peter & Feld, Lars P. & Schmidt, Christoph M. & Schnabel, Isabel & Wieland, Volker, 2018. "Vor wichtigen wirtschaftspolitischen Weichenstellungen. Jahresgutachten 2018/19 [Setting the Right Course for Economic Policy. Annual Report 2018/19]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201819, September.
    7. Gehrke, Britta & Weber, Enzo, 2018. "Identifying asymmetric effects of labor market reforms," European Economic Review, Elsevier, vol. 110(C), pages 18-40.
    8. McNeil, James, 2023. "Monetary policy and the term structure of inflation expectations with information frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    9. Yunjong Eo & James Morley, 2022. "Why Has the U.S. Economy Stagnated since the Great Recession?," The Review of Economics and Statistics, MIT Press, vol. 104(2), pages 246-258, May.
    10. 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.
    11. 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).
    12. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    13. Weiske, Sebastian, 2018. "Indicator-based estimates of the output gap in the euro area," Working Papers 12/2018, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    14. 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.
    15. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    16. Diegel, Max, 2022. "Time-varying credibility, anchoring and the Fed's inflation target," Discussion Papers 2022/9, Free University Berlin, School of Business & Economics.
    17. Adam Check & Jeremy Piger, 2021. "Structural Breaks in U.S. Macroeconomic Time Series: A Bayesian Model Averaging Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(8), pages 1999-2036, December.
    18. Mr. Geoffrey J Bannister & Mr. Harald Finger & Siddharth Kothari & Ms. Elena Loukoianova, 2020. "Addressing the Pandemic's Medium-Term Fallout in Australia and New Zealand," IMF Working Papers 2020/272, International Monetary Fund.
    19. Canova, Fabio, 2020. "FAQ: How do I measure the Output gap?," CEPR Discussion Papers 14943, C.E.P.R. Discussion Papers.
    20. Weiske, Sebastian, 2019. "Indicator-based estimates of the output gap in the euro area," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203604, Verein für Socialpolitik / German Economic Association.
    21. Manuel González-Astudillo & John M. Roberts, 2022. "When are trend–cycle decompositions of GDP reliable?," Empirical Economics, Springer, vol. 62(5), pages 2417-2460, May.
    22. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    23. Canova, Fabio, 2020. "FAQ: How do I extract the output gap?," Working Paper Series 386, Sveriges Riksbank (Central Bank of Sweden).
    24. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  26. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. Brahmana, Rayenda Khresna, 2022. "Do Machine Learning Approaches Have the Same Accuracy in Forecasting Cryptocurrencies Volatilities?," MPRA Paper 119598, University Library of Munich, Germany.
    3. Martin Iseringhausen, 2018. "The Time-Varying Asymmetry Of Exchange Rate Returns: A Stochastic Volatility – Stochastic Skewness Model," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 18/944, Ghent University, Faculty of Economics and Business Administration.
    4. Nima Nonejad, 2021. "Should crude oil price volatility receive more attention than the price of crude oil? An empirical investigation via a large‐scale out‐of‐sample forecast evaluation of US macroeconomic data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 769-791, August.
    5. William A. Barnett & Fredj Jawadi & Zied Ftiti, 2020. "Causal Relationships Between Inflation and Inflation Uncertainty," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202010, University of Kansas, Department of Economics, revised Jul 2020.
    6. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.
    7. Ran, Gao & Zixiang, Zhu & Jianhao, Lin, 2022. "Consumption–investment comovement and the dynamic impact of monetary policy uncertainty in China," Economic Modelling, Elsevier, vol. 113(C).
    8. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    9. Hassanniakalager, Arman & Baker, Paul L. & Platanakis, Emmanouil, 2024. "A False Discovery Rate approach to optimal volatility forecasting model selection," International Journal of Forecasting, Elsevier, vol. 40(3), pages 881-902.
    10. 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.
    11. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    12. 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.
    13. Töppel, Jannick & Tränkler, Timm, 2019. "Modeling energy efficiency insurances and energy performance contracts for a quantitative comparison of risk mitigation potential," Energy Economics, Elsevier, vol. 80(C), pages 842-859.
    14. Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
    15. Dunne, Peter G., 2018. "Positive Liquidity Spillovers from Sovereign Bond-Backed Securities," Research Technical Papers 5/RT/18, Central Bank of Ireland.
    16. Fernandes, Mário Correia & Dutra, Tiago Mota & Dias, José Carlos & Teixeira, João C.A., 2023. "Modelling output gaps in the Euro Area with structural breaks: The COVID-19 recession," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1046-1058.
    17. Afees A. Salisu & Ahamuefula E. Ogbonna & Tirimisiyu F. Oloko & Idris A. Adediran, 2021. "A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    18. 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).
    19. Sun, Yiqun & Ji, Hao & Cai, Xiurong & Li, Jiangchen, 2023. "Joint extreme risk of energy prices-evidence from European energy markets," Finance Research Letters, Elsevier, vol. 56(C).
    20. Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," Working Paper series 23-11, Rimini Centre for Economic Analysis.
    21. Brix, Anne Floor & Lunde, Asger & Wei, Wei, 2018. "A generalized Schwartz model for energy spot prices — Estimation using a particle MCMC method," Energy Economics, Elsevier, vol. 72(C), pages 560-582.
    22. Kubinschi Matei & Barnea Dinu & Zlatcu Iuliana, 2019. "Estimating fuel price volatility and spillover effects across different European countries," Management & Marketing, Sciendo, vol. 14(4), pages 419-430, December.
    23. Jiang, Yong & Ren, Yi-Shuai & Ma, Chao-Qun & Liu, Jiang-Long & Sharp, Basil, 2020. "Does the price of strategic commodities respond to U.S. partisan conflict?," Resources Policy, Elsevier, vol. 66(C).
    24. 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).
    25. Cronin, David & Dunne, Peter & McQuinn, Kieran, 2019. "Have Irish sovereign bonds decoupled from the euro area periphery, and why?," Papers WP625, Economic and Social Research Institute (ESRI).
    26. Guo, Li-Yang & Feng, Chao, 2021. "Are there spillovers among China's pilots for carbon emission allowances trading?," Energy Economics, Elsevier, vol. 103(C).
    27. Chen, Weidong & Xiong, Shi & Chen, Quanyu, 2022. "Characterizing the dynamic evolutionary behavior of multivariate price movement fluctuation in the carbon-fuel energy markets system from complex network perspective," Energy, Elsevier, vol. 239(PA).
    28. Paul Bui Quang & Tony Klein & Nam H. Nguyen & Thomas Walther, 2018. "Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH," JRFM, MDPI, vol. 11(2), pages 1-20, April.
    29. Zexuan Yin & Paolo Barucca, 2022. "Variational Heteroscedastic Volatility Model," Papers 2204.05806, arXiv.org.
    30. Gupta, Rangan & Nielsen, Joshua & Pierdzioch, Christian, 2024. "Stock market bubbles and the realized volatility of oil price returns," Energy Economics, Elsevier, vol. 132(C).
    31. Chang, Hao-Wen & Lin, Chinho, 2023. "Currency portfolio behavior in seven major Asian markets," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 540-559.
    32. 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.
    33. Wei Wei & Asger Lunde, 2020. "Identifying Risk Factors and Their Premia: A Study on Electricity Prices," Monash Econometrics and Business Statistics Working Papers 10/20, Monash University, Department of Econometrics and Business Statistics.
    34. 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.
    35. Chen, Rongda & Bao, Weiwei & Jin, Chenglu, 2021. "Investor sentiment and predictability for volatility on energy futures Markets: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 112-129.
    36. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2020. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," Working Papers 202077, University of Pretoria, Department of Economics.
    37. Wang, Nianling & Yin, Jiyuan & Li, Yong, 2024. "Economic policy uncertainty and stock market volatility in China: Evidence from SV-MIDAS-t model," International Review of Financial Analysis, Elsevier, vol. 92(C).
    38. Scarcioffolo, Alexandre R. & Etienne, Xiaoli L., 2021. "Regime-switching energy price volatility: The role of economic policy uncertainty," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 336-356.
    39. 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).
    40. Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian predictive distributions of oil returns using mixed data sampling volatility models," Resources Policy, Elsevier, vol. 86(PA).
    41. Baltuttis, Dennik & Töppel, Jannick & Tränkler, Timm & Wiethe, Christian, 2020. "Managing the risks of energy efficiency insurances in a portfolio context: An actuarial diversification approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    42. Muhammad Omer, 2018. "Estimating Elasticity of Transport Fuel Demand in Pakistan," Working Papers id:12811, eSocialSciences.
    43. Zouhaier Dhifaoui, 2022. "Determinism and Non-linear Behaviour of Log-return and Conditional Volatility: Empirical Analysis for 26 Stock Markets," South Asian Journal of Macroeconomics and Public Finance, , vol. 11(1), pages 69-94, June.
    44. Afees A. Salisu & Idris Adediran, 2018. "Testing for time-varying stochastic volatility in Bitcoin returns," Working Papers 060, Centre for Econometric and Allied Research, University of Ibadan.
    45. 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.
    46. Kshatriya, Saranya & Prasanna, Krishna, 2021. "Jump Interdependencies: Stochastic linkages among international stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    47. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2018. "High-frequency Cash Flow Dynamics," Working Papers 120, Brandeis University, Department of Economics and International Business School.
    48. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    49. Saranya, K. & Prasanna, P. Krishna, 2018. "Estimating stochastic volatility with jumps and asymmetry in Asian markets," Finance Research Letters, Elsevier, vol. 25(C), pages 145-153.
    50. Yong Jiang & Chao-Qun Ma & Xiao-Guang Yang & Yi-Shuai Ren, 2018. "Time-Varying Volatility Feedback of Energy Prices: Evidence from Crude Oil, Petroleum Products, and Natural Gas Using a TVP-SVM Model," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    51. Katarzyna Kuziak & Joanna Górka, 2023. "Dependence Analysis for the Energy Sector Based on Energy ETFs," Energies, MDPI, vol. 16(3), pages 1-30, January.
    52. Ulm, M. & Hambuckers, J., 2022. "Do interest rate differentials drive the volatility of exchange rates? Evidence from an extended stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 125-148.
    53. Fernando Barros & Fabio Gomes & Andre Luduvice, 2021. "The Welfare Costs of Business Cycles Unveiled: Measuring the Extent of Stabilization Policies," Working Papers 21-14R2, Federal Reserve Bank of Cleveland, revised 02 Mar 2023.
    54. Yu-Ling Hsiao, Cody & Ai, Dan & Wei, Xinyang & Sheng, Ni, 2021. "The contagious effect of China’s energy policy on stock markets: The case of the solar photovoltaic industry," Renewable Energy, Elsevier, vol. 164(C), pages 74-86.
    55. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    56. Zexuan Yin & Paolo Barucca, 2022. "Neural Generalised AutoRegressive Conditional Heteroskedasticity," Papers 2202.11285, arXiv.org.
    57. Shang, Yuhuang & Zheng, Tingguo, 2021. "Mixed-frequency SV model for stock volatility and macroeconomics," Economic Modelling, Elsevier, vol. 95(C), pages 462-472.
    58. Ahsan, Md. Nazmul & Dufour, Jean-Marie, 2021. "Simple estimators and inference for higher-order stochastic volatility models," Journal of Econometrics, Elsevier, vol. 224(1), pages 181-197.
    59. Elie Bouri & Afees A. Salisu & Rangan Gupta, 2023. "The predictive power of Bitcoin prices for the realized volatility of US stock sector returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    60. Anne Opschoor & Dewi Peerlings & Luca Rossini & Andre Lucas, 2024. "Density Forecasting for Electricity Prices under Tail Heterogeneity with the t-Riesz Distribution," Tinbergen Institute Discussion Papers 24-049/III, Tinbergen Institute.
    61. Nonejad, Nima, 2017. "Parameter instability, stochastic volatility and estimation based on simulated likelihood: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 61(C), pages 388-408.
    62. David Cronin & Peter Dunne, 2019. "Have Sovereign Bond Market Relationships Changed in the Euro Area? Evidence from Italy," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 54(4), pages 250-258, July.
    63. Gupta, Rangan & Ji, Qiang & Pierdzioch, Christian & Plakandaras, Vasilios, 2023. "Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023," Finance Research Letters, Elsevier, vol. 58(PC).
    64. Kei Nakagawa & Yusuke Uchiyama, 2020. "GO-GJRSK Model with Application to Higher Order Risk-Based Portfolio," Mathematics, MDPI, vol. 8(11), pages 1-12, November.
    65. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    66. Metiu, Norbert & Prieto, Esteban, 2023. "The macroeconomic effects of inflation uncertainty," Discussion Papers 32/2023, Deutsche Bundesbank.
    67. Jiqian Wang & Rangan Gupta & Oguzhan Cepni & Feng Ma, 2021. "Forecasting International REITs Volatility: The Role of Oil-Price Uncertainty," Working Papers 202173, University of Pretoria, Department of Economics.
    68. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Working Papers 202175, University of Pretoria, Department of Economics.
    69. Kreuzer, Alexander & Czado, Claudia, 2021. "Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 130-150.
    70. McCausland, William & Miller, Shirley & Pelletier, Denis, 2021. "Multivariate stochastic volatility using the HESSIAN method," Econometrics and Statistics, Elsevier, vol. 17(C), pages 76-94.
    71. Xu Gong & Boqiang Lin, 2022. "Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 610-640, January.
    72. 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.
    73. Zouhaier Dhifaoui, 2021. "Robustness of Detrended Cross-correlation Analysis Method Under Outliers Observations," Working Papers hal-03411380, HAL.
    74. Chen, Rongda & Xu, Jianjun, 2019. "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, vol. 78(C), pages 379-391.
    75. Sabet, Amir H. & Heaney, Richard, 2016. "An event study analysis of oil and gas firm acreage and reserve acquisitions," Energy Economics, Elsevier, vol. 57(C), pages 215-227.
    76. Jinzhi Li, 2021. "Bayesian estimation of the stochastic volatility model with double exponential jumps," Review of Derivatives Research, Springer, vol. 24(2), pages 157-172, July.
    77. 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.
    78. Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021. "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, vol. 104(C).
    79. Jiang, Yong & Zhou, Zhongbao & Liu, Qing & Lin, Ling & Xiao, Helu, 2020. "How do oil price shocks affect the output volatility of the U.S. energy mining industry? The roles of structural oil price shocks," Energy Economics, Elsevier, vol. 87(C).
    80. Hau, Liya & Zhu, Huiming & Huang, Rui & Ma, Xiang, 2020. "Heterogeneous dependence between crude oil price volatility and China’s agriculture commodity futures: Evidence from quantile-on-quantile regression," Energy, Elsevier, vol. 213(C).
    81. Schmidt, Torsten, 2018. "Inflation Expectation Uncertainty, Inflation and the Outputgap," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181575, Verein für Socialpolitik / German Economic Association.
    82. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    83. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    84. Libo Yin, 2022. "The role of intermediary capital risk in predicting oil volatility," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 401-416, January.
    85. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Mutual information and persistence in the stochastic volatility of market returns: An emergent market example," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 36-59.
    86. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2020. "Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction," Energy Economics, Elsevier, vol. 92(C).
    87. Nonejad, Nima, 2018. "Déjà vol oil? Predicting S&P 500 equity premium using crude oil price volatility: Evidence from old and recent time-series data," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 260-270.
    88. De Sola Perea, Maite & Dunne, Peter G. & Puhl, Martin & Reininger, Thomas, 2019. "Sovereign bond-backed securities: A VAR-for-VaR and marginal expected shortfall assessment," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 33-52.
    89. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    90. Będowska-Sójka, Barbara & Kliber, Agata, 2021. "Is there one safe-haven for various turbulences? The evidence from gold, Bitcoin and Ether," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    91. Chiranjivi, GVS & Sensarma, Rudra, 2023. "The effects of economic and financial shocks on private investment: A wavelet study of return and volatility spillovers," International Review of Financial Analysis, Elsevier, vol. 90(C).
    92. Ma, Xiaohan, 2023. "Oil uncertainty and the price-cost markup: Evidence from U.S. data," Energy Economics, Elsevier, vol. 124(C).
    93. Chan, Ying Tung & Dong, Yilin, 2022. "How does oil price volatility affect unemployment rates? A dynamic stochastic general equilibrium model," Economic Modelling, Elsevier, vol. 114(C).
    94. Lu Yang & Shigeyuki Hamori, 2018. "Modeling The Dynamics Of International Agricultural Commodity Prices: A Comparison Of Garch And Stochastic Volatility Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-20, September.
    95. Si, Deng-Kui & Zhao, Bing & Li, Xiao-Lin & Ding, Hui, 2021. "Policy uncertainty and sectoral stock market volatility in China," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 557-573.
    96. Chen, Zhang-HangJian & Zhao, Shou-Yu & Song, Huai-Bing & Yang, Ming-Yuan & Li, Sai-Ping, 2024. "Dynamic volatility spillover relationships between the Chinese carbon and international energy markets from extreme climate shocks," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 626-645.
    97. Mário Correia Fernandes & José Carlos Dias & João Pedro Vidal Nunes, 2024. "Performance comparison of alternative stochastic volatility models and its determinants in energy futures: COVID‐19 and Russia–Ukraine conflict features," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(3), pages 343-383, March.
    98. Huabin Bian & Renhai Hua & Qingfu Liu & Ping Zhang, 2022. "Petroleum market volatility tracker in China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(11), pages 2022-2040, November.
    99. Vasile Brătian & Ana-Maria Acu & Camelia Oprean-Stan & Emil Dinga & Gabriela-Mariana Ionescu, 2021. "Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion," Mathematics, MDPI, vol. 9(22), pages 1-20, November.

  27. Joshua C.C. Chan, 2015. "Large Bayesian VARs: A flexible Kronecker error covariance structure," CAMA Working Papers 2015-41, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Florian Huber & Gary Koop, 2021. "Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions," Papers 2107.07804, arXiv.org.
    2. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    3. 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.
    4. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2022. "Addressing COVID-19 outliers in BVARs with stochastic volatility," Discussion Papers 13/2022, Deutsche Bundesbank.
    5. Ching-Wai Chiu & Haroon Mumtaz & Gabor Pinter, 2016. "VAR Models with Non-Gaussian Shocks," Discussion Papers 1609, Centre for Macroeconomics (CFM).
    6. 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.
    7. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    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. 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.
    10. 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.
    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. 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).
    13. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    14. Jozef Barunik & Michael Ellington, 2020. "Persistence in Financial Connectedness and Systemic Risk," Papers 2007.07842, arXiv.org, revised Nov 2023.
    15. 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.
    16. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    17. Joshua C.C. Chan & Liana Jacobi & Dan Zhu, 2018. "How sensitive are VAR forecasts to prior hyperparameters? An automated sensitivity analysis," CAMA Working Papers 2018-25, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Thanh Ha, Le & Bouteska, Ahmed & Harasheh, Murad, 2024. "Dynamic connectedness between FinTech and energy markets: Evidence from fat tails, serial dependence, and Bayesian approach," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 574-586.
    19. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    20. 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.
    21. Kiss, Tamas & Nguyen, Hoang & Österholm, Pär, 2022. "Modelling Okun’s Law – Does non-Gaussianity Matter?," Working Papers 2022:1, Örebro University, School of Business.
    22. 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.
    23. Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, arXiv.org.
    24. 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.
    25. 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).
    26. 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.
    27. 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.
    28. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    29. 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.
    30. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2024. "Constructing fan charts from the ragged edge of SPF forecasts," Working Papers 2429, Banco de España.
    31. 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.
    32. Ellington, Michael & Fu, Xi & Zhu, Yunyi, 2023. "Real estate illiquidity and returns: A time-varying regional perspective," International Journal of Forecasting, Elsevier, vol. 39(1), pages 58-72.
    33. Tomasz Wo'zniak, 2024. "Fast and Efficient Bayesian Analysis of Structural Vector Autoregressions Using the R Package bsvars," Papers 2410.15090, arXiv.org.
    34. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    35. 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.
    36. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    37. 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.
    38. 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.
    39. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    40. 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.
    41. Creal, Drew & Kim, Jaeho, 2024. "Bayesian estimation of cluster covariance matrices of unknown form," Journal of Econometrics, Elsevier, vol. 241(1).
    42. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    43. 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.
    44. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    45. Ter Steege, Lucas, 2024. "Variational inference for Bayesian panel VAR models," Working Paper Series 2991, European Central Bank.
    46. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
    47. Kim, Jaeho & Linn, Scott C., 2022. "Price discovery under model uncertainty," Energy Economics, Elsevier, vol. 107(C).
    48. 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.
    49. 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.
    50. 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.
    51. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2016. "Bayesian Vector Autoregressions with Non-Gaussian Shocks," CReMFi Discussion Papers 5, CReMFi, School of Economics and Finance, QMUL.
    52. 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.
    53. Prüser, Jan, 2023. "Data-based priors for vector error correction models," International Journal of Forecasting, Elsevier, vol. 39(1), pages 209-227.
    54. Jonas E. Arias & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models," Working Papers 21-21, Federal Reserve Bank of Philadelphia.
    55. 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.
    56. 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.
    57. 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.
    58. 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.
    59. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    60. Hartwig, Benny, 2022. "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers 52/2022, Deutsche Bundesbank.
    61. 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.
    62. James Cloyne & Joseba Martinez & Haroon Mumtaz & Paolo Surico, 2024. "Taxes, Innovation and Productivity," Working Papers 979, Queen Mary University of London, School of Economics and Finance.
    63. Cappelletti, Giuseppe & Dimitrov, Ivan & Naruševičius, Laurynas & Le Grand, Catherine & Nunes, André & Podlogar, Jure & Röhm, Nicola & Ter Steege, Lucas, 2024. "2023 macroprudential stress test of the euro area banking system," Occasional Paper Series 347, European Central Bank.
    64. Ping Wu & Gary Koop, 2022. "Fast, Order-Invariant Bayesian Inference in VARs using the Eigendecomposition of the Error Covariance Matrix," Working Papers 2310, University of Strathclyde Business School, Department of Economics.
    65. Prüser, Jan & Blagov, Boris, 2022. "Improving inference and forecasting in VAR models using cross-sectional information," Ruhr Economic Papers 960, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  28. Joshua C. C. Chan & Todd E. Clark & Gary Koop, 2015. "A New Model of Inflation, Trend Inflation, and Long-Run Inflation Expectations," Working Papers (Old Series) 1520, Federal Reserve Bank of Cleveland.

    Cited by:

    1. 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.
    2. Jaromir Baxa & Miroslav Plasil & Borek Vasicek, 2013. "Inflation and the Steeplechase Between Economic Activity Variables," Working Papers 2013/15, Czech National Bank.
    3. Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
    4. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    5. Diegel, Max & Nautz, Dieter, 2020. "The role of long-term inflation expectations for the transmission of monetary policy shocks," Discussion Papers 2020/19, Free University Berlin, School of Business & Economics.
    6. Chew Lian Chua & Tim Robinson, 2018. "Why Has Australian Wages Growth Been So Low? A Phillips Curve Perspective," The Economic Record, The Economic Society of Australia, vol. 94(S1), pages 11-32, June.
    7. Arnoud Stevens & Joris Wauters, 2018. "Is euro area lowflation here to stay ? Insights from a time-varying parameter model with survey data," Working Paper Research 355, National Bank of Belgium.
    8. Lukmanova, Elizaveta & Rabitsch, Katrin, 2018. "New VAR evidence on monetary transmission channels: temporary interest rate versus inflation target shocks," Department of Economics Working Paper Series 274, WU Vienna University of Economics and Business.
    9. Geraldine Dany-Knedlik & Juan Angel Garcia, 2018. "Monetary Policy and Inflation Dynamics in ASEAN Economies," IMF Working Papers 2018/147, International Monetary Fund.
    10. Pär Österholm & Aubrey Poon, 2023. "Trend Inflation in Sweden," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4707-4716, October.
    11. McNeil, James, 2023. "Monetary policy and the term structure of inflation expectations with information frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    12. Mengheng Li & Siem Jan Koopman, 2021. "Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 614-627, August.
    13. Juan Angel Garcia & Sebastian Werner, 2018. "Inflation News and Euro Area Inflation Expectations," IMF Working Papers 2018/167, International Monetary Fund.
    14. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    15. 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.
    16. Manuel M. F. Martins & Fabio Verona, 2020. "Forecasting Inflation with the New Keynesian Phillips Curve: Frequency Matters," CEF.UP Working Papers 2001, Universidade do Porto, Faculdade de Economia do Porto.
    17. Kurozumi, Takushi & Van Zandweghe, Willem, 2022. "Macroeconomic changes with declining trend inflation: Complementarity with the superstar firm hypothesis," European Economic Review, Elsevier, vol. 141(C).
    18. Güneş Kamber & Benjamin Wong, 2018. "Global factors and trend inflation," BIS Working Papers 688, Bank for International Settlements.
    19. Nikita D. Fokin & Ekaterina V. Malikova & Andrey V. Polbin, 2024. "Time-varying parameters error correction model for real ruble exchange rate and oil prices: What has changed due to capital control and sanctions?," Russian Journal of Economics, ARPHA Platform, vol. 10(1), pages 20-33, March.
    20. 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.
    21. Richard K. Crump & Stefano Eusepi & Emanuel Moench & Bruce Preston, 2021. "The Term Structure of Expectations," Staff Reports 992, Federal Reserve Bank of New York.
    22. Christina Anderl & Guglielmo Maria Caporale, 2023. "Forecasting inflation with a zero lower bound or negative interest rates: Evidence from point and density forecasts," Manchester School, University of Manchester, vol. 91(3), pages 171-232, June.
    23. Marcelo Arbex & Sidney Caetano & Wilson Correa, 2018. "Macroeconomic Effects of Inflation Target Uncertainty Shocks," Working Papers 1804, University of Windsor, Department of Economics.
    24. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    25. Andriantomanga, Zo, 2023. "The role of survey-based expectations in real-time forecasting of US inflation," MPRA Paper 119904, University Library of Munich, Germany.
    26. Lenza, Michele & Jarociński, Marek, 2016. "An inflation-predicting measure of the output gap in the euro area," Working Paper Series 1966, European Central Bank.
    27. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    28. Marente Vlekke & Martin Mellens & Siem Jan Koopmans, 2020. "An assessment of the Phillips curve over time: evidence for the United States and the euro area," CPB Discussion Paper 416, CPB Netherlands Bureau for Economic Policy Analysis.
    29. Huber, Florian & Kaufmann, Daniel, 2016. "Trend Fundamentals and Exchange Rate Dynamics," Department of Economics Working Paper Series 214, WU Vienna University of Economics and Business.
    30. Cecchetti, Stephen & Feroli, Michael & Hooper, Peter & Kashyap, Anil & Schoenholtz, Kermit L., 2017. "Deflating Inflation Expectations: The Implications of Inflation’s Simple Dynamics," CEPR Discussion Papers 11925, C.E.P.R. Discussion Papers.
    31. Martins, Manuel Mota Freitas & Verona, Fabio, 2021. "Inflation dynamics and forecast: Frequency matters," Bank of Finland Research Discussion Papers 8/2021, Bank of Finland.
    32. Fu, Bowen, 2020. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models," Economic Modelling, Elsevier, vol. 88(C), pages 320-340.
    33. Kristin Forbes, 2019. "Has globalization changed the inflation process?," BIS Working Papers 791, Bank for International Settlements.
    34. Juan Angel Garcia & Aubrey Poon, 2022. "Inflation trends in Asia: implications for central banks [Are Phillips curves useful for forecasting inflation?]," Oxford Economic Papers, Oxford University Press, vol. 74(3), pages 671-700.
    35. Diegel, Max, 2022. "Time-varying credibility, anchoring and the Fed's inflation target," Discussion Papers 2022/9, Free University Berlin, School of Business & Economics.
    36. Francesca Rondina, 2018. "Estimating unobservable inflation expectations in the New Keynesian Phillips Curve," Working Papers 1804E, University of Ottawa, Department of Economics.
    37. Marta Banbura & Andries van Vlodrop, 2018. "Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean," Tinbergen Institute Discussion Papers 18-025/IV, Tinbergen Institute.
    38. Murasawa, Yasutomo, 2019. "Bayesian multivariate Beveridge--Nelson decomposition of I(1) and I(2) series with cointegration," MPRA Paper 91979, University Library of Munich, Germany.
    39. Dany-Knedlik, Geraldine & Holtemöller, Oliver, 2017. "Inflation dynamics during the financial crisis in Europe: Cross-sectional identification of long-run inflation expectations," IWH Discussion Papers 10/2017, Halle Institute for Economic Research (IWH).
    40. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    41. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    42. Marco Gross & Willi Semmler, 2019. "Mind the Output Gap: The Disconnect of Growth and Inflation during Recessions and Convex Phillips Curves in the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(4), pages 817-848, August.
    43. Bańbura, Marta & Bobeica, Elena, 2020. "PCCI – a data-rich measure of underlying inflation in the euro area," Statistics Paper Series 38, European Central Bank.
    44. Ellis W. Tallman & Saeed Zaman, 2015. "Forecasting Inflation: Phillips Curve Effects on Services Price Measures," Working Papers (Old Series) 1519, Federal Reserve Bank of Cleveland.
    45. Diegel, Max & Nautz, Dieter, 2021. "Long-term inflation expectations and the transmission of monetary policy shocks: Evidence from a SVAR analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
    46. Koester, Gerrit & Lis, Eliza & Nickel, Christiane & Osbat, Chiara & Smets, Frank, 2021. "Understanding low inflation in the euro area from 2013 to 2019: cyclical and structural drivers," Occasional Paper Series 280, European Central Bank.
    47. Juan Angel Garcia & Aubrey Poon, 2018. "Trend Inflation and Inflation Compensation," IMF Working Papers 2018/154, International Monetary Fund.
    48. Helder Ferreira de Mendonça & Pedro Mendes Garcia & José Valentim Machado Vicente, 2021. "Rationality and anchoring of inflation expectations: An assessment from survey‐based and market‐based measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1027-1053, September.
    49. 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.
    50. James Mitchell & Saeed Zaman, 2023. "The Distributional Predictive Content of Measures of Inflation Expectations," Working Papers 23-31, Federal Reserve Bank of Cleveland.
    51. Lukmanova, Elizaveta & Rabitsch, Katrin, 2023. "Evidence on monetary transmission and the role of imperfect information: Interest rate versus inflation target shocks," European Economic Review, Elsevier, vol. 158(C).
    52. N. Kundan Kishor & Evan F. Koenig, 2016. "The roles of inflation expectations, core inflation, and slack in real-time inflation forecasting," Working Papers 1613, Federal Reserve Bank of Dallas.
    53. Bowen Fu, Ivan Mendieta-Muñoz, 2023. "Structural shocks and trend inflation," Working Paper Series, Department of Economics, University of Utah 2023_04, University of Utah, Department of Economics.
    54. Beechey, Meredith & Österholm, Pär & Poon, Aubrey, 2023. "Estimating the US trend short-term interest rate," Finance Research Letters, Elsevier, vol. 55(PA).
    55. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  29. Joshua C.C. Chan & Eric Eisenstat, 2015. "Bayesian model comparison for time-varying parameter VARs with stochastic volatility," CAMA Working Papers 2015-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Huang, Yingying & Duan, Kun & Urquhart, Andrew, 2023. "Time-varying dependence between Bitcoin and green financial assets: A comparison between pre- and post-COVID-19 periods," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    2. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2019. "Decomposing global yield curve co-movement," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 500-513.
    3. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    4. Michael Pfarrhofer, 2019. "Measuring international uncertainty using global vector autoregressions with drifting parameters," Papers 1908.06325, arXiv.org, revised Dec 2019.
    5. Apergis Nicholas, 2021. "Forecasting US overseas travelling with univariate and multivariate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 963-976, September.
    6. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    7. Jamie L. Cross & Chenghan Hou & Bao H. Nguyen, 2018. "On the China factor in international oil markets: A regime switching approach," Working Papers No 11/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    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. Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2020. "Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 124-136, January.
    10. Rodriguez, Gabriel & Castillo B., Paul & Calero, Roberto & Salcedo Cisneros, Rodrigo & Ataurima Arellano, Miguel, 2024. "Evolution of the exchange rate pass-through into prices in Peru: An empirical application using TVP-VAR-SV models," Journal of International Money and Finance, Elsevier, vol. 142(C).
    11. Ioannou Demosthenes & Pagliari Maria Sole & Stracca Livio, 2020. "The international dimension of a fragile EMU," Working papers 795, Banque de France.
    12. 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.
    13. 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.
    14. Zhao, Junming & Zhang, Tianding, 2023. "Exploring the time-varying dependence between Bitcoin and the global stock market: Evidence from a TVP-VAR approach," Finance Research Letters, Elsevier, vol. 58(PA).
    15. Zhu, Qinwen & Diao, Xundi & Wu, Chongfeng, 2023. "Volatility forecast with the regularity modifications," Finance Research Letters, Elsevier, vol. 58(PA).
    16. 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.
    17. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.
    18. 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.
    19. Bäurle Gregor & Kaufmann Daniel & Kaufmann Sylvia & Strachan Rodney, 2020. "Constrained interest rates and changing dynamics at the zero lower bound," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-26, April.
    20. Cao, Guangxi & Xie, Wenhao, 2022. "Asymmetric dynamic spillover effect between cryptocurrency and China's financial market: Evidence from TVP-VAR based connectedness approach," Finance Research Letters, Elsevier, vol. 49(C).
    21. Gabriel Rodríguez & Renato Vassallo, 2022. "Time Evolution of External Shocks on Macroeconomic Fluctuations in Pacific Alliance Countries: Empirical Application using TVP-VAR-SV Models," Documentos de Trabajo / Working Papers 2022-508, Departamento de Economía - Pontificia Universidad Católica del Perú.
    22. Yang, Xite & Zhang, Qin & Liu, Haiyue & Liu, Zihan & Tao, Qiufan & Lai, Yongzeng & Huang, Linya, 2024. "Economic policy uncertainty, macroeconomic shocks, and systemic risk: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    23. Kiss, Tamas & Nguyen, Hoang & Österholm, Pär, 2022. "Modelling Okun’s Law – Does non-Gaussianity Matter?," Working Papers 2022:1, Örebro University, School of Business.
    24. Robert Lehmann & Ida Wikman, 2022. "Quarterly GDP Estimates for the German States," ifo Working Paper Series 370, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    25. Lyu, Xiaoyi & Hu, Hao, 2024. "The dynamic impact of monetary policy on stock market liquidity," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 388-405.
    26. Aubrey Poon, 2018. "The transmission mechanism of Malaysian monetary policy: a time-varying vector autoregression approach," Empirical Economics, Springer, vol. 55(2), pages 417-444, September.
    27. 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).
    28. 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.
    29. 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.
    30. 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).
    31. O'Brien, Martin & Velasco, Sofia, 2020. "Unobserved components models with stochastic volatility for extracting trends and cycles in credit," Research Technical Papers 09/RT/20, Central Bank of Ireland.
    32. Karlsson, Sune & Österholm, Pär, 2018. "Is the US Phillips Curve Stable? Evidence from Bayesian VARs," Working Papers 2018:5, Örebro University, School of Business.
    33. Karlsson, Sune & Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2024. "US Interest Rates: Are Relations Stable?," Working Papers 2024:3, Örebro University, School of Business.
    34. 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.
    35. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    36. 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).
    37. Jiménez, Alvaro & Rodríguez, Gabriel & Ataurima Arellano, Miguel, 2023. "Time-varying impact of fiscal shocks over GDP growth in Peru: An empirical application using hybrid TVP-VAR-SV models," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 314-332.
    38. 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.
    39. 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.
    40. Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2020. "Can Volatility Solve the Naive Portfolio Puzzle?," Papers 2005.03204, arXiv.org, revised Feb 2022.
    41. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    42. Mauricio Alvarado & Gabriel Rodríguez, 2024. "Time-Varying Effects of Financial Uncertainty Shocks on Macroeconomic Fluctuations in Peru," Documentos de Trabajo / Working Papers 2024-531, Departamento de Economía - Pontificia Universidad Católica del Perú.
    43. Flavio Pérez Rojo & Gabriel Rodríguez, 2023. "Jane Haldimand Marcet: Impact of Monetary Policy Shocks in the Peruvian Economy Over Time," Documentos de Trabajo / Working Papers 2023-523, Departamento de Economía - Pontificia Universidad Católica del Perú.
    44. Gabriel Rodríguez & Paulo Chávez, 2022. "Time Changing Effects of External Shocks on Macroeconomic Fluctuations in Peru: Empirical Application Using Regime-Switching VAR Models with Stochastic Volatility," Documentos de Trabajo / Working Papers 2022-509, Departamento de Economía - Pontificia Universidad Católica del Perú.
    45. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    46. 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.
    47. 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.
    48. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
    49. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    50. Nikita Moiseev & Aleksander Sorokin & Natalya Zvezdina & Alexey Mikhaylov & Lyubov Khomyakova & Mir Sayed Shah Danish, 2021. "Credit Risk Theoretical Model on the Base of DCC-GARCH in Time-Varying Parameters Framework," Mathematics, MDPI, vol. 9(19), pages 1-12, September.
    51. 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.
    52. Saranya, K. & Prasanna, P. Krishna, 2018. "Estimating stochastic volatility with jumps and asymmetry in Asian markets," Finance Research Letters, Elsevier, vol. 25(C), pages 145-153.
    53. Stylianos Asimakopoulos & Marco Lorusso & Francesco Ravazzolo, 2023. "A Bayesian DSGE Approach to Modelling Cryptocurrency," Working Papers No 09/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    54. Fu, Bowen, 2020. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models," Economic Modelling, Elsevier, vol. 88(C), pages 320-340.
    55. Joshua C.C. Chan & Eric Eisenstat, 2018. "Comparing hybrid time-varying parameter VARs," CAMA Working Papers 2018-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    56. Prüser, Jan, 2021. "The horseshoe prior for time-varying parameter VARs and Monetary Policy," Journal of Economic Dynamics and Control, Elsevier, vol. 129(C).
    57. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    58. Wenying Zeng & Songbai Song & Yan Kang & Xuan Gao & Rui Ma, 2022. "Response of Runoff to Meteorological Factors Based on Time-Varying Parameter Vector Autoregressive Model with Stochastic Volatility in Arid and Semi-Arid Area of Weihe River Basin," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    59. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    60. Helmut Lutkepohl & Fei Shang & Luis Uzeda & Tomasz Wo'zniak, 2024. "Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference," Papers 2404.11057, arXiv.org.
    61. Karlsson, Sune & Österholm, Pär, 2018. "A Note on the Stability of the Swedish Philips Curve," Working Papers 2018:6, Örebro University, School of Business.
    62. 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.
    63. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
    64. Gabriel Rodriguez & Paul Castillo B. & Junior A. Ojeda Cunya, 2024. "Time-Varying Effects of External Shocks on Macroeconomic Fluctuations in Peru: An Empirical Application using TVP-VAR-SV Models," Open Economies Review, Springer, vol. 35(5), pages 1015-1050, November.
    65. Mauro Bernardi & Daniele Bianchi & Nicolas Bianco, 2022. "Variational inference for large Bayesian vector autoregressions," Papers 2202.12644, arXiv.org, revised Jun 2023.
    66. Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.
    67. Dieppe, Alistair & van Roye, Björn & Legrand, Romain, 2016. "The BEAR toolbox," Working Paper Series 1934, European Central Bank.
    68. Bognanni, Mark & Zito, John, 2020. "Sequential Bayesian inference for vector autoregressions with stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    69. Alexander Meléndez Holguín & Gabriel Rodríguez, 2023. "Evolution over time of the effects of fiscal shocks in the peruvian economy: empirical application using TVP-VAR-SV models," Documentos de Trabajo / Working Papers 2023-516, Departamento de Economía - Pontificia Universidad Católica del Perú.
    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. Karlsson, Sune & Mazur, Stepan, 2020. "Flexible Fat-tailed Vector Autoregression," Working Papers 2020:5, Örebro University, School of Business.
    72. Romain Aumond & Julien Royer, 2024. "Improving the robustness of Markov-switching dynamic factor models with time-varying volatility," Working Papers 2024-04, Center for Research in Economics and Statistics.
    73. 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.
    74. 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.
    75. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2021. "Vector autoregression models with skewness and heavy tails," Working Papers 2021:8, Örebro University, School of Business.
    76. 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.
    77. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    78. Fu, Bowen, 2023. "Measuring the trend real interest rate in a data-rich environment," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    79. 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.
    80. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.
    81. Zhou, Dong-hai & Liu, Xiao-xing & Tang, Chun & Yang, Guang-yi, 2023. "Time-varying risk spillovers in Chinese stock market – New evidence from high-frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    82. 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.
    83. Cross, Jamie L. & Hou, Chenghan & Nguyen, Bao H., 2021. "On the China factor in the world oil market: A regime switching approach11We thank Hilde Bjørnland, Tatsuyoshi Okimoto, Ippei Fujiwara, Knut Aastveit, Leif Anders Thorsrud, Francesco Ravazzolo, Renee ," Energy Economics, Elsevier, vol. 95(C).
    84. Rodríguez, Gabriel & Vassallo, Renato & Castillo B., Paul, 2023. "Effects of external shocks on macroeconomic fluctuations in Pacific Alliance countries," Economic Modelling, Elsevier, vol. 124(C).
    85. Ioannou, Demosthenes & Stracca, Livio & Pagliari, Maria Sole, 2020. "The international dimension of an incomplete EMU," Working Paper Series 2459, European Central Bank.
    86. Laurynas Narusevicius & Tomas Ramanauskas & Laura Gudauskaitė & Tomas Reichenbachas, 2019. "Lithuanian house price index: modelling and forecasting," Bank of Lithuania Occasional Paper Series 28, Bank of Lithuania.
    87. Dorra Zouari & Achraf Ghorbel & Sonia Ghorbel-Zouari & Younes Boujelbène, 2014. "Volatility spillovers and dynamic correlation between liquidity risk factors in Tunisian banks," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 6(1), pages 1-26.
    88. Chen, Bin-xia & Sun, Yan-lin, 2024. "Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).

  30. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
    2. 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.
    3. Julien Champagne & Guillaume Poulin‐Bellisle & Rodrigo Sekkel, 2020. "Introducing the Bank of Canada staff economic projections database," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 114-129, January.
    4. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    5. Mónica Correa-López & Matías Pacce & Kathi Schlepper, 2019. "Exploring trend inFLation dynamics in Euro Area countries," Working Papers 1909, Banco de España.
    6. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    7. Cross, Jamie L. & Hou, Chenghan & Trinh, Kelly, 2021. "Returns, volatility and the cryptocurrency bubble of 2017–18," Economic Modelling, Elsevier, vol. 104(C).
    8. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic Forecasting in a Multi-country Context," Working Papers 22-02, Federal Reserve Bank of Cleveland.
    9. Lenza, Michele & Jarociński, Marek, 2016. "An inflation-predicting measure of the output gap in the euro area," Working Paper Series 1966, European Central Bank.
    10. Stephen Wright & James Mitchell & Donald Robertson, 2018. "R2 bounds for predictive models: what univariate properties tell us about multivariate predictability," Birkbeck Working Papers in Economics and Finance 1804, Birkbeck, Department of Economics, Mathematics & Statistics.
    11. Afees A. Salisu & Idris Adediran, 2018. "Testing for time-varying stochastic volatility in Bitcoin returns," Working Papers 060, Centre for Econometric and Allied Research, University of Ibadan.
    12. 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.
    13. Martins, Manuel Mota Freitas & Verona, Fabio, 2021. "Inflation dynamics and forecast: Frequency matters," Bank of Finland Research Discussion Papers 8/2021, Bank of Finland.
    14. Fu, Bowen, 2020. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models," Economic Modelling, Elsevier, vol. 88(C), pages 320-340.
    15. Balatti, Mirco, 2020. "Inflation volatility in small and large advanced open economies," Working Paper Series 2448, European Central Bank.
    16. Amaze Lusompa & Sai Sattiraju, 2023. "Will High Underlying Inflation Persist?," Economic Bulletin, Federal Reserve Bank of Kansas City, pages 1-4, May.
    17. Helmut Lutkepohl & Fei Shang & Luis Uzeda & Tomasz Wo'zniak, 2024. "Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference," Papers 2404.11057, arXiv.org.
    18. Michal Franta & Ivan Sutoris, 2020. "Dynamics of Czech Inflation: The Role of the Trend and the Cycle," Working Papers 2020/1, Czech National Bank.
    19. 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.
    20. Alex, Dony, 2021. "Anchoring of inflation expectations in large emerging economies," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    21. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2018. "Does time-variation matter in the stochastic volatility components for G7 stock returns," Working Papers 062, Centre for Econometric and Allied Research, University of Ibadan.
    22. Chi-Young Choi & Alexander Chudik & Aaron Smallwood, 2024. "Time-varying Persistence of House Price Growth: The Role of Expectations and Credit Supply," Globalization Institute Working Papers 426, Federal Reserve Bank of Dallas.

  31. Joshua C.C. Chan, 2015. "The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling," CAMA Working Papers 2015-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. Diego Ferreira & Andreza Aparecida Palma, 2018. "Inflation And Inflation Uncertainty In Latin America: A Time-Varying Stochastic Volatility In Mean Approach," Anais do XLIV Encontro Nacional de Economia [Proceedings of the 44th Brazilian Economics Meeting] 125, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    3. Dominik Bertsche & Robin Braun, 2018. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Working Paper Series of the Department of Economics, University of Konstanz 2018-03, Department of Economics, University of Konstanz.
    4. Dimitrakopoulos, Stefanos, 2017. "Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility," Economics Letters, Elsevier, vol. 150(C), pages 10-14.
    5. Dimitrakopoulos, Stefanos, 2017. "The semiparametric asymmetric stochastic volatility model with time-varying parameters: The case of US inflation," Economics Letters, Elsevier, vol. 155(C), pages 14-18.
    6. William A. Barnett & Fredj Jawadi & Zied Ftiti, 2020. "Causal Relationships Between Inflation and Inflation Uncertainty," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202010, University of Kansas, Department of Economics, revised Jul 2020.
    7. Michael Pfarrhofer, 2019. "Measuring international uncertainty using global vector autoregressions with drifting parameters," Papers 1908.06325, arXiv.org, revised Dec 2019.
    8. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    9. Mehmet Balcilar & Zeynel Abidin Ozdemir, 2017. "The nexus between the oil price and its volatility in a stochastic volatility in mean model with time-varying parameters," Working Papers 15-33, Eastern Mediterranean University, Department of Economics.
    10. Nonejad, Nima, 2014. "Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks," MPRA Paper 55664, University Library of Munich, Germany.
    11. Markku Lanne & Jani Luoto, 2017. "A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 969-995, August.
    12. Emanuela Ciapanna & Marco Taboga, 2019. "Bayesian Analysis of Coefficient Instability in Dynamic Regressions," Econometrics, MDPI, vol. 7(3), pages 1-32, June.
    13. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    14. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    15. Haroon Mumtaz & Konstantinos Theodoridis, 2015. "Dynamic Effects of Monetary Policy Shocks on Macroeconomic Volatility," Working Papers 760, Queen Mary University of London, School of Economics and Finance.
    16. 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.
    17. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 772-802.
    18. Randal J. Verbrugge & Saeed Zaman, 2022. "Improving Inflation Forecasts Using Robust Measures," Working Papers 22-23R, Federal Reserve Bank of Cleveland, revised 30 May 2023.
    19. Abanto-Valle, Carlos A. & Rodríguez, Gabriel & Garrafa-Aragón, Hernán B., 2021. "Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 272-286.
    20. Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
    21. María T. González-Pérez, 2021. "Lessons from estimating the average option-implied volatility term structure for the Spanish banking sector," Working Papers 2128, Banco de España.
    22. Philipp Otto & Osman Dou{g}an & Suleyman Tac{s}p{i}nar & Wolfgang Schmid & Anil K. Bera, 2023. "Spatial and Spatiotemporal Volatility Models: A Review," Papers 2308.13061, arXiv.org.
    23. Forbes, Kristin & Kirkham, Lewis & Theodoridis, Konstantinos, 2017. "A trendy approach to UK inflation dynamics," Discussion Papers 49, Monetary Policy Committee Unit, Bank of England.
    24. Daichi Hiraki & Siddhartha Chib & Yasuhiro Omori, 2024. "Stochastic Volatility in Mean: Efficient Analysis by a Generalized Mixture Sampler," Papers 2404.13986, arXiv.org, revised Nov 2024.
    25. Mengheng Li & Siem Jan Koopman, 2021. "Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 614-627, August.
    26. Danilo Cascaldi-Garcia & Deepa Dhume Datta & Thiago Revil T. Ferreira & Olesya V. Grishchenko & Mohammad R. Jahan-Parvar & Juan M. Londono & Francesca Loria & Sai Ma & Marius del Giudice Rodriguez & J, 2020. "What is Certain about Uncertainty?," International Finance Discussion Papers 1294, Board of Governors of the Federal Reserve System (U.S.).
      • 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.
    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. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    29. Jiang, Yong & Ren, Yi-Shuai & Ma, Chao-Qun & Liu, Jiang-Long & Sharp, Basil, 2020. "Does the price of strategic commodities respond to U.S. partisan conflict?," Resources Policy, Elsevier, vol. 66(C).
    30. Mehmet Balcilar & Zeynel Abidin Ozdemir, 2020. "A re-examination of growth and growth uncertainty relationship in a stochastic volatility in the mean model with time-varying parameters," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(3), pages 611-641, August.
    31. 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.
    32. Christophe Chesneau & Salima El Kolei & Fabien Navarro, 2022. "Parametric estimation of hidden Markov models by least squares type estimation and deconvolution," Statistical Papers, Springer, vol. 63(5), pages 1615-1648, October.
    33. 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.
    34. 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.
    35. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    36. Mehmet Balcilar & Zeynel Abidin Ozdemir, 2018. "The volatility effect on precious metals prices in a stochastic volatility in mean model with time-varying parameters," Working Papers 15-34, Eastern Mediterranean University, Department of Economics.
    37. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Stephen M. Miller & Rangan Gupta, 2019. "Time–frequency relationship between US inflation and inflation uncertainty: evidence from historical data," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(5), pages 673-702, November.
    38. 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.
    39. Florian Huber & Michael Pfarrhofer, 2020. "Dynamic shrinkage in time-varying parameter stochastic volatility in mean models," Papers 2005.06851, arXiv.org.
    40. Markku Lanne & Jani Luoto, 2016. "Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression," CREATES Research Papers 2016-04, Department of Economics and Business Economics, Aarhus University.
    41. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    42. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    43. Vivek Sharma & Edgar Silgado-Gómez, 2019. "Sovereign Spread Volatility and Banking Sector," CEIS Research Paper 454, Tor Vergata University, CEIS, revised 08 Mar 2019.
    44. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    45. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    46. Carlos A. Abanto-Valle & Gabriel Rodríguez & Hernán B. Garrafa-Aragón, 2020. "Stochastic Volatility in Mean: Empirical Evidence from Stock Latin American Markets," Documentos de Trabajo / Working Papers 2020-481, Departamento de Economía - Pontificia Universidad Católica del Perú.
    47. 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.
    48. Claudiu Tiberiu Albulescu & Cornel Oros, 2020. "Inflation, uncertainty, and labour market conditions in the US," Post-Print hal-03558119, HAL.
    49. Zied Ftiti & Fredj Jawadi, 2019. "Forecasting Inflation Uncertainty in the United States and Euro Area," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 455-476, June.
    50. 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.
    51. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    52. Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2019. "The nexus between the oil price and its volatility risk in a stochastic volatility in the mean model with time-varying parameters," Resources Policy, Elsevier, vol. 61(C), pages 572-584.
    53. Yong Jiang & Chao-Qun Ma & Xiao-Guang Yang & Yi-Shuai Ren, 2018. "Time-Varying Volatility Feedback of Energy Prices: Evidence from Crude Oil, Petroleum Products, and Natural Gas Using a TVP-SVM Model," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    54. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    55. Emmanuel C. Mamatzakis & Mike G. Tsionas, 2021. "A Bayesian panel stochastic volatility measure of financial stability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5363-5384, October.
    56. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    57. Ahsan, Md. Nazmul & Dufour, Jean-Marie, 2021. "Simple estimators and inference for higher-order stochastic volatility models," Journal of Econometrics, Elsevier, vol. 224(1), pages 181-197.
    58. Joshua C.C. Chan & Yong Song, 2018. "Measuring Inflation Expectations Uncertainty Using High‐Frequency Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(6), pages 1139-1166, September.
    59. Himounet, Nicolas, 2022. "Searching the nature of uncertainty: Macroeconomic and financial risks VS geopolitical and pandemic risks," International Economics, Elsevier, vol. 170(C), pages 1-31.
    60. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    61. Terence D. Agbeyegbe, 2023. "The Link Between Output Growth and Output Growth Volatility: Barbados," Annals of Data Science, Springer, vol. 10(3), pages 787-804, June.
    62. 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.
    63. Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.
    64. Meng Yan & Kai Shi, 2024. "Revisiting the Impact of US Uncertainty Shocks: New Evidence from China’s Investment Dynamics," Open Economies Review, Springer, vol. 35(3), pages 457-495, July.
    65. Dimitrakopoulos, Stefanos & Dey, Dipak K., 2017. "Discrete-response state space models with conditional heteroscedasticity: An application to forecasting the federal funds rate target," Economics Letters, Elsevier, vol. 154(C), pages 20-23.
    66. Armin Pourkhanali & Jonathan Keith & Xibin Zhang, 2021. "Conditional Heteroscedasticity Models with Time-Varying Parameters: Estimation and Asymptotics," Monash Econometrics and Business Statistics Working Papers 15/21, Monash University, Department of Econometrics and Business Statistics.
    67. Romain Aumond & Julien Royer, 2024. "Improving the robustness of Markov-switching dynamic factor models with time-varying volatility," Working Papers 2024-04, Center for Research in Economics and Statistics.
    68. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    69. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    70. Mamatzakis, Emmanuel C. & Ongena, Steven & Tsionas, Mike G., 2021. "Does alternative finance moderate bank fragility? Evidence from the euro area," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    71. Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2019. "The volatility effect on precious metals price returns in a stochastic volatility in mean model with time-varying parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    72. Mehmet Balcilar & Rangan Gupta & Shixuan Wang & Mark E. Wohar, 2019. "Oil Price Uncertainty and Movements in the US Government Bond Risk Premia," Working Papers 201919, University of Pretoria, Department of Economics.
    73. Carlos A. Abanto-Valle & Gabriel Rodríguez & Luis M. Castro Cepero & Hernán B. Garrafa-Aragón, 2024. "Approximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Emerging and Developed Markets," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1775-1801, September.
    74. Hau, Liya & Zhu, Huiming & Huang, Rui & Ma, Xiang, 2020. "Heterogeneous dependence between crude oil price volatility and China’s agriculture commodity futures: Evidence from quantile-on-quantile regression," Energy, Elsevier, vol. 213(C).
    75. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    76. Smith, Michael Stanley & Maneesoonthorn, Worapree, 2018. "Inversion copulas from nonlinear state space models with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 34(3), pages 389-407.
    77. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    78. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    79. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Mutual information and persistence in the stochastic volatility of market returns: An emergent market example," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 36-59.
    80. 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.
    81. Nonejad, Nima, 2018. "Déjà vol oil? Predicting S&P 500 equity premium using crude oil price volatility: Evidence from old and recent time-series data," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 260-270.
    82. 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.
    83. 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.
    84. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.
    85. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    86. Grant, Angelia L., 2018. "The Great Recession and Okun's law," Economic Modelling, Elsevier, vol. 69(C), pages 291-300.
    87. Nima Nonejad, 2019. "Has the 2008 financial crisis and its aftermath changed the impact of inflation on inflation uncertainty in member states of the european monetary union?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(2), pages 246-276, May.
    88. Fernandes, Cecilia Melo, 2021. "ECB communication as a stabilization and coordination device: evidence from ex-ante inflation uncertainty," Working Paper Series 2582, European Central Bank.
    89. Nonejad, Nima, 2020. "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, vol. 71(C).
    90. Arce-Alfaro, Gabriel & Blagov, Boris, 2021. "Monetary policy uncertainty and inflation expectations," Ruhr Economic Papers 899, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    91. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.
    92. Gregor Kastner & Sylvia Fruhwirth-Schnatter & Hedibert Freitas Lopes, 2016. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models," Papers 1602.08154, arXiv.org, revised Jul 2017.

  32. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2014. "Large Bayesian VARMAs," SIRE Discussion Papers 2015-06, Scottish Institute for Research in Economics (SIRE).

  33. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2014. "Large Bayesian VARMAs," SIRE Discussion Papers 2015-06, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    2. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    3. Leonardo Nogueira Ferreira & Silvia Miranda-Agrippino & Giovanni Ricco, 2023. "Bayesian Local Projections," Working Papers Series 581, Central Bank of Brazil, Research Department.
    4. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2017. "The transmission of monetary policy shocks," Bank of England working papers 657, Bank of England.
    5. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian vector autoregressions," LSE Research Online Documents on Economics 87393, London School of Economics and Political Science, LSE Library.
    6. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    7. 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.
    8. 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.
    9. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    11. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    12. Benati, Luca & Chan, Joshua & Eisenstat, Eric & Koop, Gary, 2020. "Identifying noise shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    13. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    14. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    15. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.

  34. Joshua C.C. Chan & Angelia L. Grant, 2014. "Fast Computation of the Deviance Information Criterion for Latent Variable Models," CAMA Working Papers 2014-09, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. 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.
    2. Melolinna, Marko & Tóth, Máté, 2019. "Trend and cycle shocks in Bayesian unobserved components models for UK productivity," Bank of England working papers 826, Bank of England.
    3. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    4. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An extended Markov-Switching Dynamic Factor Model," OECD Statistics Working Papers 2020/01, OECD Publishing.
    5. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    6. Rodriguez, Gabriel & Castillo B., Paul & Calero, Roberto & Salcedo Cisneros, Rodrigo & Ataurima Arellano, Miguel, 2024. "Evolution of the exchange rate pass-through into prices in Peru: An empirical application using TVP-VAR-SV models," Journal of International Money and Finance, Elsevier, vol. 142(C).
    7. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 772-802.
    8. Arnab Kumar Maity & Sanjib Basu & Santu Ghosh, 2021. "Bayesian criterion‐based variable selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 835-857, August.
    9. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Gabriel Rodríguez & Renato Vassallo, 2022. "Time Evolution of External Shocks on Macroeconomic Fluctuations in Pacific Alliance Countries: Empirical Application using TVP-VAR-SV Models," Documentos de Trabajo / Working Papers 2022-508, Departamento de Economía - Pontificia Universidad Católica del Perú.
    11. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2014. "Large Bayesian VARMAs," SIRE Discussion Papers 2015-06, Scottish Institute for Research in Economics (SIRE).
    12. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    13. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    14. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    15. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    16. Mauricio Alvarado & Gabriel Rodríguez, 2024. "Time-Varying Effects of Financial Uncertainty Shocks on Macroeconomic Fluctuations in Peru," Documentos de Trabajo / Working Papers 2024-531, Departamento de Economía - Pontificia Universidad Católica del Perú.
    17. Georges Bresson & Anoop Chaturvedi & Mohammad Arshad Rahman & Shalabh, 2020. "Seemingly Unrelated Regression with Measurement Error: Estimation via Markov chain Monte Carlo and Mean Field Variational Bayes Approximation," Papers 2006.07074, arXiv.org.
    18. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    19. Adam, Marc C. & Jansson, Walter, 2019. "Credit constraints and the propagation of the Great Depression in Germany," Discussion Papers 2019/12, Free University Berlin, School of Business & Economics.
    20. Stylianos Asimakopoulos & Marco Lorusso & Francesco Ravazzolo, 2023. "A Bayesian DSGE Approach to Modelling Cryptocurrency," Working Papers No 09/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    21. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    22. Joshua C.C. Chan & Eric Eisenstat, 2018. "Comparing hybrid time-varying parameter VARs," CAMA Working Papers 2018-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    23. Diegel, Max, 2022. "Time-varying credibility, anchoring and the Fed's inflation target," Discussion Papers 2022/9, Free University Berlin, School of Business & Economics.
    24. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    25. Fang Liu & Xiaojing Wang & Roeland Hancock & Ming-Hui Chen, 2022. "Bayesian Model Assessment for Jointly Modeling Multidimensional Response Data with Application to Computerized Testing," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1290-1317, December.
    26. Gabriel Rodriguez & Paul Castillo B. & Junior A. Ojeda Cunya, 2024. "Time-Varying Effects of External Shocks on Macroeconomic Fluctuations in Peru: An Empirical Application using TVP-VAR-SV Models," Open Economies Review, Springer, vol. 35(5), pages 1015-1050, November.
    27. 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.
    28. Alexander Meléndez Holguín & Gabriel Rodríguez, 2023. "Evolution over time of the effects of fiscal shocks in the peruvian economy: empirical application using TVP-VAR-SV models," Documentos de Trabajo / Working Papers 2023-516, Departamento de Economía - Pontificia Universidad Católica del Perú.
    29. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    30. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.
    31. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    32. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Martijn Huisman & Martijn Heymans & Jos Twisk, 2022. "Bayesian model selection for multilevel mediation models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 219-235, May.
    33. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    34. Rodríguez, Gabriel & Vassallo, Renato & Castillo B., Paul, 2023. "Effects of external shocks on macroeconomic fluctuations in Pacific Alliance countries," Economic Modelling, Elsevier, vol. 124(C).
    35. Nima Nonejad, 2019. "Has the 2008 financial crisis and its aftermath changed the impact of inflation on inflation uncertainty in member states of the european monetary union?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(2), pages 246-276, May.
    36. 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).
    37. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Adrian Quintero, 2022. "Bayesian Model Selection for Longitudinal Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 516-547, November.
    38. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.

  35. Joshua C.C. Chan & Rodney Strachan, 2014. "The Zero Lower Bound: Implications for Modelling the Interest Rate," Working Paper series 42_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Benjamin K. Johannsen & Elmar Mertens, 2016. "A Time Series Model of Interest Rates With the Effective Lower Bound," Finance and Economics Discussion Series 2016-033, Board of Governors of the Federal Reserve System (U.S.).
    2. Caggiano, Giovanni & Castelnuovo, Efrem & Pellegrino, Giovanni, 2017. "Estimating the real effects of uncertainty shocks at the zero lower bound," Bank of Finland Research Discussion Papers 6/2017, Bank of Finland.
    3. Joshua C.C. Chan, 2015. "The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling," CAMA Working Papers 2015-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.
    5. Eric Eisenstat & Joshua C.C. Chan & Rodney Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," Working Paper series 44_14, Rimini Centre for Economic Analysis.
    6. 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.
    7. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2023. "Shadow-rate VARs," Discussion Papers 14/2023, Deutsche Bundesbank.
    8. Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2019. "The volatility effect on precious metals price returns in a stochastic volatility in mean model with time-varying parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    9. 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.).

  36. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    2. Abdelhakim Aknouche, 2017. "Periodic autoregressive stochastic volatility," Statistical Inference for Stochastic Processes, Springer, vol. 20(2), pages 139-177, July.
    3. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Tao Zeng & Yong Li & Jun Yu, 2014. "Deviance Information Criterion for Comparing VAR Models," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 33, pages 615-637, Emerald Group Publishing Limited.
    5. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    6. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    7. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    9. Aknouche, Abdelhakim, 2013. "Periodic autoregressive stochastic volatility," MPRA Paper 69571, University Library of Munich, Germany, revised 2015.
    10. Lu Yang & Shigeyuki Hamori, 2018. "Modeling The Dynamics Of International Agricultural Commodity Prices: A Comparison Of Garch And Stochastic Volatility Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-20, September.

  37. 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.

    Cited by:

    1. Niko Hauzenberger & Florian Huber & Gary Koop, 2020. "Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods," Papers 2005.03906, arXiv.org, revised May 2023.
    2. Mike G. Tsionas, 2016. "Alternative Bayesian compression in Vector Autoregressions and related models," Working Papers 216, Bank of Greece.
    3. Lin Liu, 2022. "Economic Uncertainty and Exchange Market Pressure: Evidence From China," SAGE Open, , vol. 12(1), pages 21582440211, January.
    4. Sylvia Fruhwirth-Schnatter & Peter Knaus, 2022. "Sparse Bayesian State-Space and Time-Varying Parameter Models," Papers 2207.12147, arXiv.org.
    5. 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.
    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. Dawid J. van Lill, 2017. "Changes in the Liquidity Effect Over Time: Evidence from Four Monetary Policy Regimes," Working Papers 704, Economic Research Southern Africa.
    8. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    9. 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.
    10. Tsai, I-Chun & Chen, Han-Bo & Lin, Che-Chun, 2024. "The ability of energy commodities to hedge the dynamic risk of epidemic black swans," Resources Policy, Elsevier, vol. 89(C).
    11. Mike G. Tsionas, 2016. "Alternatives to large VAR, VARMA and multivariate stochastic volatility models," Working Papers 217, Bank of Greece.
    12. 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.
    13. 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.
    14. Karlsson, Sune & Österholm, Pär, 2018. "Is the US Phillips Curve Stable? Evidence from Bayesian VARs," Working Papers 2018:5, Örebro University, School of Business.
    15. 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.
    16. 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.
    17. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. 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.
    19. Korobilis, Dimitris, 2014. "Data-based priors for vector autoregressions with drifting coefficients," SIRE Discussion Papers 2014-022, Scottish Institute for Research in Economics (SIRE).
    20. Lopes, Hedibert F. & McCulloch, Robert E. & Tsay, Ruey S., 2022. "Parsimony inducing priors for large scale state–space models," Journal of Econometrics, Elsevier, vol. 230(1), pages 39-61.
    21. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
    22. Williams Ohemeng & Elvis Kwame Agyapong & Kenneth Ofori-Boateng, 2021. "Exchange rate and inflation dynamics: does the month or quarter of the year matter?," SN Business & Economics, Springer, vol. 1(6), pages 1-24, June.
    23. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    24. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    25. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    26. Marta Banbura & Andries van Vlodrop, 2018. "Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean," Tinbergen Institute Discussion Papers 18-025/IV, Tinbergen Institute.
    27. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
    28. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    29. 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.
    30. Reusens Peter & Croux Christophe, 2017. "Detecting time variation in the price puzzle: a less informative prior choice for time varying parameter VAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    31. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    32. Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021. "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, vol. 101(C).
    33. Bitto, Angela & Frühwirth-Schnatter, Sylvia, 2019. "Achieving shrinkage in a time-varying parameter model framework," Journal of Econometrics, Elsevier, vol. 210(1), pages 75-97.
    34. 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.
    35. Cross, Jamie, 2019. "On the reduced macroeconomic volatility of the Australian economy: Good policy or good luck?," Economic Modelling, Elsevier, vol. 77(C), pages 174-186.
    36. Annalisa Cadonna & Sylvia Frühwirth-Schnatter & Peter Knaus, 2020. "Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models," Econometrics, MDPI, vol. 8(2), pages 1-36, May.
    37. 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.
    38. 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.
    39. Liu, Junbin & Liu, Xiaoxing & Shi, Guangping, 2019. "What influences portfolio contagion among open-end mutual funds?," Finance Research Letters, Elsevier, vol. 30(C), pages 145-152.
    40. Phan, Tuan, 2016. "Has Monetary Policy Become More Aggressive, But Less Effective Over Time?," MPRA Paper 107200, University Library of Munich, Germany.

  38. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    3. Martin Iseringhausen, 2018. "The Time-Varying Asymmetry Of Exchange Rate Returns: A Stochastic Volatility – Stochastic Skewness Model," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 18/944, Ghent University, Faculty of Economics and Business Administration.
    4. William A. Barnett & Fredj Jawadi & Zied Ftiti, 2020. "Causal Relationships Between Inflation and Inflation Uncertainty," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202010, University of Kansas, Department of Economics, revised Jul 2020.
    5. Nonejad, Nima, 2014. "Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks," MPRA Paper 55664, University Library of Munich, Germany.
    6. 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.
    7. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    8. Boriss Siliverstovs & Daniel S. Wochner, 2021. "State‐dependent evaluation of predictive ability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 547-574, April.
    9. 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.
    10. Marc Joëts & Valérie Mignon & Tovonony Razafindrabe, 2015. "Does the volatility of commodity prices reflect macroeconomic uncertainty?," Working Papers 2015-02, CEPII research center.
    11. Bo Zhang & Jamie Cross & Na Guo, 2020. "Time-Varying Trend Models for Forecasting Inflation in Australia," Working Papers No 09/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    12. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    13. Iseringhausen, Martin, 2024. "A time-varying skewness model for Growth-at-Risk," International Journal of Forecasting, Elsevier, vol. 40(1), pages 229-246.
    14. 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.
    15. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    16. Aubrey Poon, 2018. "The transmission mechanism of Malaysian monetary policy: a time-varying vector autoregression approach," Empirical Economics, Springer, vol. 55(2), pages 417-444, September.
    17. Boriss Siliverstovs & Daniel Wochner, 2019. "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," KOF Working papers 19-463, KOF Swiss Economic Institute, ETH Zurich.
    18. Knut Are Aastveit & Jamie Cross & Herman K. van Dijk, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Tinbergen Institute Discussion Papers 21-053/III, Tinbergen Institute.
    19. Kai Carstensen & Leonard Salzmann, 2016. "The G7 Business Cycle in a Globalized World," CESifo Working Paper Series 5980, CESifo.
    20. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    21. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    22. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    23. Roberto Casarin & Domenico Sartore & Marco Tronzano, 2018. "A Bayesian Markov-Switching Correlation Model for Contagion Analysis on Exchange Rate Markets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 101-114, January.
    24. 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.
    25. Clements, Michael P. & Galvão, Ana Beatriz, 2017. "Model and survey estimates of the term structure of US macroeconomic uncertainty," International Journal of Forecasting, Elsevier, vol. 33(3), pages 591-604.
    26. 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.
    27. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," Working Papers 2020-01, University of Tasmania, Tasmanian School of Business and Economics.
    28. Jinzhi Li, 2021. "Bayesian estimation of the stochastic volatility model with double exponential jumps," Review of Derivatives Research, Springer, vol. 24(2), pages 157-172, July.
    29. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    30. 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.
    31. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    32. Eymen Errais & Dhikra Bahri, 2016. "Is Standard Deviation a Good Measure of Volatility? the Case of African Markets with Price Limits," Annals of Economics and Finance, Society for AEF, vol. 17(1), pages 145-165, May.
    33. Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    34. Ciccarelli, Matteo & García, Juan Angel & Montes-Galdón, Carlos, 2017. "Unconventional monetary policy and the anchoring of inflation expectations," Working Paper Series 1995, European Central Bank.

  39. Joshua C.C. Chan & Eric Eisenstat, 2013. "Gibbs Samplers for VARMA and Its Extensions," ANU Working Papers in Economics and Econometrics 2013-604, Australian National University, College of Business and Economics, School of Economics.

    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. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).

  40. Joshua C.C. Chan & Roberto Leon-Gonzalez & Rodney W. Strachan, 2013. "Invariant Inference and Efficient Computation in the Static Factor Model," CAMA Working Papers 2013-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Sylvia Kaufmann & Markus Pape, 2024. "A geometric approach to factor model identification," Working Papers 24.06, Swiss National Bank, Study Center Gerzensee.
    2. Mengheng Li & Marcel Scharth, 2022. "Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochastic Volatility Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 285-301, January.
    3. KiHoon Jimmy Hong & Bin Peng & Xiaohui Zhang, 2015. "Capturing the Impact of Unobserved Sector-Wide Shocks on Stock Returns with Panel Data Model," The Economic Record, The Economic Society of Australia, vol. 91(295), pages 495-508, December.
    4. 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.
    5. 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.
    6. Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, arXiv.org.
    7. 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.
    8. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    9. 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.
    10. KiHoon Jimmy Hong & Bin Peng & Xiaohui Zhang, 2014. "Capturing the Impact of Latent Industry-Wide Shocks with Dynamic Panel Model," Research Paper Series 347, Quantitative Finance Research Centre, University of Technology, Sydney.
    11. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2014. "Bayesian analysis of dynamic factor models: An ex-post approach towards the rotation problem," Kiel Working Papers 1902, Kiel Institute for the World Economy (IfW Kiel).
    12. Bin Peng & Giovanni Forchini, 2014. "Consistent Estimation of Panel Data Models with a Multifactor Error Structure when the Cross Section Dimension is Large," Working Paper Series 20, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    13. Sylvia Fruhwirth-Schnatter & Darjus Hosszejni & Hedibert Freitas Lopes, 2023. "When it counts -- Econometric identification of the basic factor model based on GLT structures," Papers 2301.06354, arXiv.org.
    14. Sylvia Kaufmann & Markus Pape, 2023. "Bayesian (non-)unique sparse factor modelling," Working Papers 23.04, Swiss National Bank, Study Center Gerzensee.
    15. 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.
    16. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    17. 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.
    18. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
    19. 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.
    20. Christian Aßmann & Jens Boysen-Hogrefe & Markus Pape, 2024. "Post-processing for Bayesian analysis of reduced rank regression models with orthonormality restrictions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(3), pages 577-609, September.
    21. Sylvia Kaufmann & Christian Schumacher, 2013. "Bayesian estimation of sparse dynamic factor models with order-independent identification," Working Papers 13.04, Swiss National Bank, Study Center Gerzensee.

  41. Joshua C.C. Chan & Cody Yu-Ling Hsiao & Renée A. Fry-McKibbin, 2013. "A Regime Switching Skew-normal Model for Measuring Financial Crisis and Contagion," CAMA Working Papers 2013-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Renée Fry-McKibbin & Cody Hsiao & Chrismin Tang, 2014. "Contagion and Global Financial Crises: Lessons from Nine Crisis Episodes," Open Economies Review, Springer, vol. 25(3), pages 521-570, July.
    2. Hsiao, Cody Yu-Ling & Chen, Hsing Hung, 2018. "The contagious effects on economic development after resuming construction policy for nuclear power plants in Coastal China," Energy, Elsevier, vol. 152(C), pages 291-302.
    3. Cody Yu-Ling Hsiao & James Morley, 2022. "Debt and financial market contagion," Empirical Economics, Springer, vol. 62(4), pages 1599-1648, April.

  42. Joshua C.C. Chan & Gary Koop, 2013. "Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables," ANU Working Papers in Economics and Econometrics 2013-603, Australian National University, College of Business and Economics, School of Economics.

    Cited by:

    1. Louzis Dimitrios P., 2016. "Steady-state priors and Bayesian variable selection in VAR forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 495-527, December.
    2. Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," Working Paper series 18-12, Rimini Centre for Economic Analysis.
    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. Máximo Camacho & María Dolores Gadea & Ana Gómez Loscos, 2019. "A new approach to dating the reference cycle," Working Papers 1914, Banco de España.
    5. Chiara Perricone, 2013. "Clustering Macroeconomic Variables," CEIS Research Paper 283, Tor Vergata University, CEIS, revised 11 Jun 2013.
    6. 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.
    7. Dimitrios P. Louzis, 2016. "Macroeconomic forecasting and structural changes in steady states," Working Papers 204, Bank of Greece.

  43. Joshua C C Chan, 2012. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," ANU Working Papers in Economics and Econometrics 2012-591, Australian National University, College of Business and Economics, School of Economics.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. 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.
    3. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    4. Dominik Bertsche & Robin Braun, 2018. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Working Paper Series of the Department of Economics, University of Konstanz 2018-03, Department of Economics, University of Konstanz.
    5. 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.
    6. William A. Barnett & Fredj Jawadi & Zied Ftiti, 2020. "Causal Relationships Between Inflation and Inflation Uncertainty," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202010, University of Kansas, Department of Economics, revised Jul 2020.
    7. Dimitris Korobilis & Maximilian Schröder, 2023. "Monitoring multicountry macroeconomic risk," Working Paper 2023/9, Norges Bank.
    8. Wang, Renhe & Wang, Tong & Qian, Zhiyong & Hu, Shulan, 2023. "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast," Finance Research Letters, Elsevier, vol. 58(PC).
    9. Nonejad, Nima, 2014. "Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks," MPRA Paper 55664, University Library of Munich, Germany.
    10. Markku Lanne & Jani Luoto, 2017. "A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 969-995, August.
    11. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    13. Joshua C.C. Chan, 2015. "The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling," CAMA Working Papers 2015-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Töppel, Jannick & Tränkler, Timm, 2019. "Modeling energy efficiency insurances and energy performance contracts for a quantitative comparison of risk mitigation potential," Energy Economics, Elsevier, vol. 80(C), pages 842-859.
    15. Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
    16. Philipp Otto & Osman Dou{g}an & Suleyman Tac{s}p{i}nar & Wolfgang Schmid & Anil K. Bera, 2023. "Spatial and Spatiotemporal Volatility Models: A Review," Papers 2308.13061, arXiv.org.
    17. Mengheng Li & Siem Jan Koopman, 2021. "Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 614-627, August.
    18. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    19. 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.
    20. Bo Zhang & Jamie Cross & Na Guo, 2020. "Time-Varying Trend Models for Forecasting Inflation in Australia," Working Papers No 09/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    21. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2014. "Large Bayesian VARMAs," SIRE Discussion Papers 2015-06, Scottish Institute for Research in Economics (SIRE).
    22. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.
    23. 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.
    24. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.
    25. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    26. 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.
    27. Chang, Hao-Wen & Lin, Chinho, 2023. "Currency portfolio behavior in seven major Asian markets," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 540-559.
    28. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    29. 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.
    30. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    31. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    32. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2016. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," CESifo Working Paper Series 5759, CESifo.
    33. 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).
    34. 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.
    35. 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.
    36. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    37. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    38. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    39. 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.
    40. Hsiao, Cody Yu-Ling & Chen, Hsing Hung, 2018. "The contagious effects on economic development after resuming construction policy for nuclear power plants in Coastal China," Energy, Elsevier, vol. 152(C), pages 291-302.
    41. Frazier, David T. & Maneesoonthorn, Worapree & Martin, Gael M. & McCabe, Brendan P.M., 2019. "Approximate Bayesian forecasting," International Journal of Forecasting, Elsevier, vol. 35(2), pages 521-539.
    42. Zied Ftiti & Fredj Jawadi, 2019. "Forecasting Inflation Uncertainty in the United States and Euro Area," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 455-476, June.
    43. Joshua C.C. Chan & Eric Eisenstat, 2013. "Gibbs Samplers for VARMA and Its Extensions," ANU Working Papers in Economics and Econometrics 2013-604, Australian National University, College of Business and Economics, School of Economics.
    44. Baltuttis, Dennik & Töppel, Jannick & Tränkler, Timm & Wiethe, Christian, 2020. "Managing the risks of energy efficiency insurances in a portfolio context: An actuarial diversification approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    45. Zouhaier Dhifaoui, 2022. "Determinism and Non-linear Behaviour of Log-return and Conditional Volatility: Empirical Analysis for 26 Stock Markets," South Asian Journal of Macroeconomics and Public Finance, , vol. 11(1), pages 69-94, June.
    46. Hervé Le Bihan & Danilo Leiva-León & Matías Pacce, 2023. "Underlying inflation and asymetric risks," Working Papers 2319, Banco de España.
    47. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    48. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    49. 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.
    50. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    51. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    52. 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.
    53. 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.
    54. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    55. 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.
    56. Eric Eisenstat & Rodney Strachan, 2014. "Modelling Inflation Volatility," Working Paper series 43_14, Rimini Centre for Economic Analysis.
    57. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    58. Terence D. Agbeyegbe, 2023. "The Link Between Output Growth and Output Growth Volatility: Barbados," Annals of Data Science, Springer, vol. 10(3), pages 787-804, June.
    59. Chan, Joshua C.C. & Santi, Caterina, 2021. "Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    60. 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.
    61. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," Working Papers 2020-01, University of Tasmania, Tasmanian School of Business and Economics.
    62. Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.
    63. Nima Nonejad, 2021. "Using the conditional volatility channel to improve the accuracy of aggregate equity return predictions," Empirical Economics, Springer, vol. 61(2), pages 973-1009, August.
    64. 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.
    65. Jihyun Park & Andrey Sarantsev, 2024. "The VIX as Stochastic Volatility for Corporate Bonds," Papers 2410.22498, arXiv.org, revised Nov 2024.
    66. Daniel Kaufmann, 2019. "Nominal stability over two centuries," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 155(1), pages 1-23, December.
    67. Nima Nonejad, 2013. "Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach," CREATES Research Papers 2013-26, Department of Economics and Business Economics, Aarhus University.
    68. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    69. Juan Angel García & Sebastian E. V. Werner, 2021. "Inflation News and Euro-Area Inflation Expectations," International Journal of Central Banking, International Journal of Central Banking, vol. 17(3), pages 1-60, September.
    70. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    71. 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.
    72. Jiang, Yong & Zhou, Zhongbao & Liu, Qing & Lin, Ling & Xiao, Helu, 2020. "How do oil price shocks affect the output volatility of the U.S. energy mining industry? The roles of structural oil price shocks," Energy Economics, Elsevier, vol. 87(C).
    73. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    74. Qureshi, Irfan, 2015. "What are monetary policy shocks?," The Warwick Economics Research Paper Series (TWERPS) 1086, University of Warwick, Department of Economics.
    75. Nonejad, Nima, 2018. "Déjà vol oil? Predicting S&P 500 equity premium using crude oil price volatility: Evidence from old and recent time-series data," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 260-270.
    76. Na Guo & Bo Zhang & Jamie L. Cross, 2022. "Time‐varying trend models for forecasting inflation in Australia," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 316-330, March.
    77. Bo Zhang, 2019. "Real‐time inflation forecast combination for time‐varying coefficient models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 175-191, April.
    78. 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.
    79. Nima Nonejad, 2019. "Has the 2008 financial crisis and its aftermath changed the impact of inflation on inflation uncertainty in member states of the european monetary union?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(2), pages 246-276, May.
    80. Lu Yang & Shigeyuki Hamori, 2018. "Modeling The Dynamics Of International Agricultural Commodity Prices: A Comparison Of Garch And Stochastic Volatility Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-20, September.
    81. Qureshi, Irfan, 2015. "What are monetary policy shocks?," Economic Research Papers 270008, University of Warwick - Department of Economics.
    82. Nonejad, Nima, 2020. "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, vol. 71(C).
    83. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.
    84. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  44. Chan, Joshua & Koop, Gary & Potter, Simon, 2012. "A New Model Of Trend Inflation," SIRE Discussion Papers 2012-12, Scottish Institute for Research in Economics (SIRE).

    Cited by:

    1. Petrella, Ivan & Delle Monache, Davide, 2016. "Adaptive models and heavy tails," Bank of England working papers 577, Bank of England.
    2. Huber, Florian & Onorante, Luca & Pfarrhofer, Michael, 2024. "Forecasting euro area inflation using a huge panel of survey expectations," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1042-1054.
    3. 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.
    4. Mónica Correa-López & Matías Pacce & Kathi Schlepper, 2019. "Exploring trend inFLation dynamics in Euro Area countries," Working Papers 1909, Banco de España.
    5. Cogley, Timothy & Sargent, Thomas J. & Surico, Paolo, 2015. "Price-level uncertainty and instability in the United Kingdom," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 1-16.
    6. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Stephen M. Miller & Rangan Gupta, 2016. "Time-Frequency Relationship between Inflation and Inflation Uncertainty for the U.S.: Evidence from Historical Data," Working papers 2016-12, University of Connecticut, Department of Economics.
    7. Markku Lanne & Jani Luoto, 2017. "A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 969-995, August.
    8. Benjamin Wong, 2015. "Do Inflation Expectations Propagate the Inflationary Impact of Real Oil Price Shocks?: Evidence from the Michigan Survey," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(8), pages 1673-1689, December.
    9. Claudiu Tiberiu Albulescu & Christian Aubin & Daniel Goyeau, 2017. "Stock prices, inflation and inflation uncertainty in the U.S.: testing the long-run relationship considering Dow Jones sector indexes," Applied Economics, Taylor & Francis Journals, vol. 49(18), pages 1794-1807, April.
    10. Arnoud Stevens & Joris Wauters, 2018. "Is euro area lowflation here to stay ? Insights from a time-varying parameter model with survey data," Working Paper Research 355, National Bank of Belgium.
    11. Joshua C.C. Chan, 2015. "The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling," CAMA Working Papers 2015-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Mountford, Andrew, 2024. "Economic Growth Analysis When Balanced Growth Paths May Be Time Varying," MPRA Paper 119938, University Library of Munich, Germany.
    13. McNeil, James, 2023. "Monetary policy and the term structure of inflation expectations with information frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    14. Forbes, Kristin & Kirkham, Lewis & Theodoridis, Konstantinos, 2017. "A trendy approach to UK inflation dynamics," Discussion Papers 49, Monetary Policy Committee Unit, Bank of England.
    15. Yunjong Eo & Luis Uzeda & Benjamin Wong, 2022. "Understanding trend inflation through the lens of the goods and services sectors," CAMA Working Papers 2022-28, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    16. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Kajuth, Florian, 2010. "NAIRU estimates for Germany: New evidence on the inflation-unemployment trade-off," Discussion Paper Series 1: Economic Studies 2010,19, Deutsche Bundesbank.
    18. Güneş Kamber & Benjamin Wong, 2018. "Global factors and trend inflation," BIS Working Papers 688, Bank for International Settlements.
    19. Franz Xaver Zobl & Martin Ertl, 2021. "The Condemned Live Longer – New Evidence of the New Keynesian Phillips Curve in Central and Eastern Europe," Open Economies Review, Springer, vol. 32(4), pages 671-699, September.
    20. Andreza A Palma, 2016. "Natural interest rate in Brazil: further evidence frThe main objective of this study is to estimate the natural interest rate for Brazil using a parsimonious AR-trend-bound model proposed by Chan, Koo," Economics Bulletin, AccessEcon, vol. 36(3), pages 1306-1314.
    21. Kabundi, Alain & Mlachila, Montfort, 2019. "The role of monetary policy credibility in explaining the decline in exchange rate pass-through in South Africa," Economic Modelling, Elsevier, vol. 79(C), pages 173-185.
    22. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    23. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    24. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Stephen M. Miller & Rangan Gupta, 2019. "Time–frequency relationship between US inflation and inflation uncertainty: evidence from historical data," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(5), pages 673-702, November.
    25. Günes Kamber & Benjamin Wong, 2016. "Testing an Interpretation of Core Inflation Measures in New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2016/06, Reserve Bank of New Zealand.
    26. 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.
    27. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
    28. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    29. De Schryder, Selien & Peersman, Gert & Wauters, Joris, 2020. "Wage indexation and the monetary policy regime," Journal of Macroeconomics, Elsevier, vol. 63(C).
    30. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    31. Joshua C. C. Chan & Aubrey Poon & Dan Zhu, 2023. "High-Dimensional Conditionally Gaussian State Space Models with Missing Data," Papers 2302.03172, arXiv.org.
    32. Stephen Wright & James Mitchell & Donald Robertson, 2018. "R2 bounds for predictive models: what univariate properties tell us about multivariate predictability," Birkbeck Working Papers in Economics and Finance 1804, Birkbeck, Department of Economics, Mathematics & Statistics.
    33. Claudiu Tiberiu Albulescu & Cornel Oros, 2020. "Inflation, uncertainty, and labour market conditions in the US," Post-Print hal-03558119, HAL.
    34. 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.
    35. Marente Vlekke & Martin Mellens & Siem Jan Koopmans, 2020. "An assessment of the Phillips curve over time: evidence for the United States and the euro area," CPB Discussion Paper 416, CPB Netherlands Bureau for Economic Policy Analysis.
    36. 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.
    37. Timothy Cogley & Thomas J. Sargent, 2014. "Measuring Price-Level Uncertainty and Instability in the U.S., 1850-2012," Working Papers 2014-33, Economic Research Institute, Bank of Korea.
    38. Hervé Le Bihan & Danilo Leiva-León & Matías Pacce, 2023. "Underlying inflation and asymetric risks," Working Papers 2319, Banco de España.
    39. 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.
    40. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2023. "Shadow-rate VARs," Discussion Papers 14/2023, Deutsche Bundesbank.
    41. Petrella, Ivan & Venditti, Fabrizio & Delle Monache, Davide, 2016. "Adaptive state space models with applications to the business cycle and financial stress," CEPR Discussion Papers 11599, C.E.P.R. Discussion Papers.
    42. Joshua C.C. Chan, 2013. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," CAMA Working Papers 2013-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    43. Juan Angel Garcia & Aubrey Poon, 2022. "Inflation trends in Asia: implications for central banks [Are Phillips curves useful for forecasting inflation?]," Oxford Economic Papers, Oxford University Press, vol. 74(3), pages 671-700.
    44. 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.
    45. Gimeno, Ricardo & Ibáñez, Alfredo, 2018. "The eurozone (expected) inflation: An option's eyes view," Journal of International Money and Finance, Elsevier, vol. 86(C), pages 70-92.
    46. 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.
    47. Eric Eisenstat & Rodney Strachan, 2014. "Modelling Inflation Volatility," Working Paper series 43_14, Rimini Centre for Economic Analysis.
    48. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    49. 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.
    50. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    51. Behera, Harendra Kumar & Patra, Michael Debabrata, 2022. "Measuring trend inflation in India," Journal of Asian Economics, Elsevier, vol. 80(C).
    52. Dany-Knedlik, Geraldine & Holtemöller, Oliver, 2017. "Inflation dynamics during the financial crisis in Europe: Cross-sectional identification of long-run inflation expectations," IWH Discussion Papers 10/2017, Halle Institute for Economic Research (IWH).
    53. Terence D. Agbeyegbe, 2023. "The Link Between Output Growth and Output Growth Volatility: Barbados," Annals of Data Science, Springer, vol. 10(3), pages 787-804, June.
    54. 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.
    55. Markku Lanne & Jani Luoto, 2014. "Noncausal Bayesian Vector Autoregression," CREATES Research Papers 2014-07, Department of Economics and Business Economics, Aarhus University.
    56. Alex, Dony, 2021. "Anchoring of inflation expectations in large emerging economies," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    57. Niko Hauzenberger & Daniel Kaufmann & Rebecca Stuart & Cédric Tille, 2022. "What Drives Long-Term Interest Rates? Evidence from the Entire Swiss Franc History 1852-2020," IRENE Working Papers 22-03, IRENE Institute of Economic Research.
    58. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    59. Juan Angel Garcia & Aubrey Poon, 2018. "Trend Inflation and Inflation Compensation," IMF Working Papers 2018/154, International Monetary Fund.
    60. Hau, Liya & Zhu, Huiming & Huang, Rui & Ma, Xiang, 2020. "Heterogeneous dependence between crude oil price volatility and China’s agriculture commodity futures: Evidence from quantile-on-quantile regression," Energy, Elsevier, vol. 213(C).
    61. Markku Lanne & Jani Luoto & Henri Nyberg, 2014. "Is the Quantity Theory of Money Useful in Forecasting U.S. Inflation?," CREATES Research Papers 2014-26, Department of Economics and Business Economics, Aarhus University.
    62. Jeremy J. Nalewaik, 2015. "Regime-Switching Models for Estimating Inflation Uncertainty," Finance and Economics Discussion Series 2015-93, Board of Governors of the Federal Reserve System (U.S.).
    63. Chew Lian Chua & Sarantis Tsiaplias, 2014. "A Bayesian Approach to Modelling Bivariate Time-Varying Cointegration and Cointegrating Rank," Melbourne Institute Working Paper Series wp2014n27, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    64. 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.
    65. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
    66. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.
    67. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  45. Joshua C C Chan & Eric Eisenstat, 2012. "Marginal Likelihood Estimation with the Cross-Entropy Method," CAMA Working Papers 2012-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Melolinna, Marko & Tóth, Máté, 2019. "Trend and cycle shocks in Bayesian unobserved components models for UK productivity," Bank of England working papers 826, Bank of England.
    2. 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.
    3. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    4. Anders Warne & Günter Coenen & Kai Christoffel, 2017. "Marginalized Predictive Likelihood Comparisons of Linear Gaussian State‐Space Models with Applications to DSGE, DSGE‐VAR, and VAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 103-119, January.
    5. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2020. "On an integer-valued stochastic intensity model for time series of counts," MPRA Paper 105406, University Library of Munich, Germany.
    6. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    7. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    8. Rodriguez, Gabriel & Castillo B., Paul & Calero, Roberto & Salcedo Cisneros, Rodrigo & Ataurima Arellano, Miguel, 2024. "Evolution of the exchange rate pass-through into prices in Peru: An empirical application using TVP-VAR-SV models," Journal of International Money and Finance, Elsevier, vol. 142(C).
    9. Töppel, Jannick & Tränkler, Timm, 2019. "Modeling energy efficiency insurances and energy performance contracts for a quantitative comparison of risk mitigation potential," Energy Economics, Elsevier, vol. 80(C), pages 842-859.
    10. Fernandes, Mário Correia & Dutra, Tiago Mota & Dias, José Carlos & Teixeira, João C.A., 2023. "Modelling output gaps in the Euro Area with structural breaks: The COVID-19 recession," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1046-1058.
    11. Jiang, Yong & Ren, Yi-Shuai & Ma, Chao-Qun & Liu, Jiang-Long & Sharp, Basil, 2020. "Does the price of strategic commodities respond to U.S. partisan conflict?," Resources Policy, Elsevier, vol. 66(C).
    12. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    13. Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, arXiv.org.
    14. Angelia L. Grant, 2017. "The Early Millennium Slowdown: Replicating the Peersman (2005) Results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 224-232, January.
    15. Karlsson, Sune & Österholm, Pär, 2018. "Is the US Phillips Curve Stable? Evidence from Bayesian VARs," Working Papers 2018:5, Örebro University, School of Business.
    16. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    17. Jiménez, Alvaro & Rodríguez, Gabriel & Ataurima Arellano, Miguel, 2023. "Time-varying impact of fiscal shocks over GDP growth in Peru: An empirical application using hybrid TVP-VAR-SV models," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 314-332.
    18. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    19. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2020. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," Working Papers 202077, University of Pretoria, Department of Economics.
    20. Flavio Pérez Rojo & Gabriel Rodríguez, 2023. "Jane Haldimand Marcet: Impact of Monetary Policy Shocks in the Peruvian Economy Over Time," Documentos de Trabajo / Working Papers 2023-523, Departamento de Economía - Pontificia Universidad Católica del Perú.
    21. Baltuttis, Dennik & Töppel, Jannick & Tränkler, Timm & Wiethe, Christian, 2020. "Managing the risks of energy efficiency insurances in a portfolio context: An actuarial diversification approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    22. Gabriel Rodríguez & Paulo Chávez, 2022. "Time Changing Effects of External Shocks on Macroeconomic Fluctuations in Peru: Empirical Application Using Regime-Switching VAR Models with Stochastic Volatility," Documentos de Trabajo / Working Papers 2022-509, Departamento de Economía - Pontificia Universidad Católica del Perú.
    23. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    24. Joshua C.C. Chan & Eric Eisenstat, 2018. "Comparing hybrid time-varying parameter VARs," CAMA Working Papers 2018-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    25. Diegel, Max, 2022. "Time-varying credibility, anchoring and the Fed's inflation target," Discussion Papers 2022/9, Free University Berlin, School of Business & Economics.
    26. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    27. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    28. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    29. Bowen Fu, 2019. "Bubbles and crises: Replicating the Anundsen et al. (2016) results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 822-826, August.
    30. Gupta, Rangan & Ji, Qiang & Pierdzioch, Christian & Plakandaras, Vasilios, 2023. "Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023," Finance Research Letters, Elsevier, vol. 58(PC).
    31. Gabriel Rodriguez & Paul Castillo B. & Junior A. Ojeda Cunya, 2024. "Time-Varying Effects of External Shocks on Macroeconomic Fluctuations in Peru: An Empirical Application using TVP-VAR-SV Models," Open Economies Review, Springer, vol. 35(5), pages 1015-1050, November.
    32. Liangliang Zhou & Yishao Shi & Xiangyang Cao, 2019. "Evaluation of Land Intensive Use in Shanghai Pilot Free Trade Zone," Land, MDPI, vol. 8(6), pages 1-16, May.
    33. Alexander Meléndez Holguín & Gabriel Rodríguez, 2023. "Evolution over time of the effects of fiscal shocks in the peruvian economy: empirical application using TVP-VAR-SV models," Documentos de Trabajo / Working Papers 2023-516, Departamento de Economía - Pontificia Universidad Católica del Perú.
    34. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    35. Jiang, Yong & Zhou, Zhongbao & Liu, Qing & Lin, Ling & Xiao, Helu, 2020. "How do oil price shocks affect the output volatility of the U.S. energy mining industry? The roles of structural oil price shocks," Energy Economics, Elsevier, vol. 87(C).
    36. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2018. "Does time-variation matter in the stochastic volatility components for G7 stock returns," Working Papers 062, Centre for Econometric and Allied Research, University of Ibadan.
    37. Sahar Abbas & Fahimeh Moosavi, 2012. "Finding shortest path in static networks: using a modified algorithm," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 1(1), pages 29-34, January.
    38. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Mutual information and persistence in the stochastic volatility of market returns: An emergent market example," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 36-59.
    39. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2013. "Predictive likelihood comparisons with DSGE and DSGE-VAR models," Working Paper Series 1536, European Central Bank.
    40. Rodríguez, Gabriel & Vassallo, Renato & Castillo B., Paul, 2023. "Effects of external shocks on macroeconomic fluctuations in Pacific Alliance countries," Economic Modelling, Elsevier, vol. 124(C).
    41. Lu Yang & Shigeyuki Hamori, 2018. "Modeling The Dynamics Of International Agricultural Commodity Prices: A Comparison Of Garch And Stochastic Volatility Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-20, September.
    42. Mário Correia Fernandes & José Carlos Dias & João Pedro Vidal Nunes, 2024. "Performance comparison of alternative stochastic volatility models and its determinants in energy futures: COVID‐19 and Russia–Ukraine conflict features," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(3), pages 343-383, March.
    43. Tomáš Jeřábek & Radka Šperková, 2015. "A Predictive Likelihood Approach to Bayesian Averaging," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 63(4), pages 1269-1276.
    44. Dante A. Urbina & Gabriel Rodríguez, 2023. "Evolution of the effects of mineral commodity prices on fiscal fluctuations: empirical evidence from TVP-VAR-SV models for Peru," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 159(1), pages 153-184, February.

  46. Joshua C.C. Chan & Justin L. Tobias, 2012. "Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments," ANU Working Papers in Economics and Econometrics 2012-580, Australian National University, College of Business and Economics, School of Economics.

    Cited by:

    1. Xiaoyi Han & Lung-Fei Lee, 2016. "Bayesian Analysis of Spatial Panel Autoregressive Models With Time-Varying Endogenous Spatial Weight Matrices, Common Factors, and Random Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 642-660, October.
    2. Jiti Gao & Bin Peng & Zhao Ren & Xiaohui Zhang, 2015. "Variable Selection for a Categorical Varying-Coefficient Model with Identifications for Determinants of Body Mass Index," Monash Econometrics and Business Statistics Working Papers 21/15, Monash University, Department of Econometrics and Business Statistics.
    3. Iordanis Parikoglou & Grigorios Emvalomatis & Doris Läpple & Fiona Thorne & Michael Wallace, 2024. "The contribution of innovation to farm-level productivity," Journal of Productivity Analysis, Springer, vol. 62(2), pages 239-255, October.

  47. Joshua C C Chan & Gary Koop & Simon M Potter, 2012. "A Bounded Model of Time Variation in Trend Inflation, NAIRU and the Phillips Curve," ANU Working Papers in Economics and Econometrics 2012-590, Australian National University, College of Business and Economics, School of Economics.

    Cited by:

    1. Jaromir Baxa & Miroslav Plasil & Borek Vasicek, 2013. "Inflation and the Steeplechase Between Economic Activity Variables," Working Papers 2013/15, Czech National Bank.
    2. Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
    3. Kabundi, Alain & Poon, Aubrey & Wu, Ping, 2023. "A time-varying Phillips curve with global factors: Are global factors important?," Economic Modelling, Elsevier, vol. 126(C).
    4. Mallick, Debdulal, 2019. "Policy regimes and the shape of the Phillips curve in Australia," Journal of Policy Modeling, Elsevier, vol. 41(6), pages 1077-1094.
    5. Arnoud Stevens & Joris Wauters, 2018. "Is euro area lowflation here to stay ? Insights from a time-varying parameter model with survey data," Working Paper Research 355, National Bank of Belgium.
    6. 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.
    7. Sune Karlsson & Pär Österholm, 2023. "Is the US Phillips curve stable? Evidence from Bayesian vector autoregressions," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(1), pages 287-314, January.
    8. Yunjong Eo & Luis Uzeda & Benjamin Wong, 2022. "Understanding trend inflation through the lens of the goods and services sectors," CAMA Working Papers 2022-28, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. 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.
    10. Güneş Kamber & Benjamin Wong, 2018. "Global factors and trend inflation," BIS Working Papers 688, Bank for International Settlements.
    11. Jinho Choi & Juan Carlos Escanciano & Junjie Guo, 2022. "Generalized band spectrum estimation with an application to the New Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1055-1078, August.
    12. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    13. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    14. 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.
    15. Tino Berger & Gerdie Everaert & Hauke Vierke, 2015. "Testing for time variation in an unobserved components model for the U.S. economy," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 15/903, Ghent University, Faculty of Economics and Business Administration.
    16. 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.
    17. Lenza, Michele & Jarociński, Marek, 2016. "An inflation-predicting measure of the output gap in the euro area," Working Paper Series 1966, European Central Bank.
    18. Huber, Florian & Kaufmann, Daniel, 2016. "Trend Fundamentals and Exchange Rate Dynamics," Department of Economics Working Paper Series 214, WU Vienna University of Economics and Business.
    19. Jaeho Kim & Sora Chon, 2022. "Bayesian estimation of the long-run trend of the US economy," Empirical Economics, Springer, vol. 62(2), pages 461-485, February.
    20. Kim, Insu & Yie, Myung-Soo, 2016. "Trend inflation, firms' backward-looking behavior, and inflation gap persistence," Economic Modelling, Elsevier, vol. 58(C), pages 116-125.
    21. N. Cordemans & J. Wauters, 2018. "Are inflation and economic activity out of sync in the euro area?," Economic Review, National Bank of Belgium, issue i, pages 79-96, June.
    22. Hervé Le Bihan & Danilo Leiva-León & Matías Pacce, 2023. "Underlying inflation and asymetric risks," Working Papers 2319, Banco de España.
    23. 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.
    24. 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.
    25. Fu, Bowen, 2020. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models," Economic Modelling, Elsevier, vol. 88(C), pages 320-340.
    26. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    27. Karlsson, Sune & Österholm, Pär, 2018. "A Note on the Stability of the Swedish Philips Curve," Working Papers 2018:6, Örebro University, School of Business.
    28. 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.
    29. Abhishek K. Umrawal & Joshua C. C. Chan, 2021. "On Parameter Estimation in Unobserved Components Models subject to Linear Inequality Constraints," Papers 2110.12149, arXiv.org, revised Feb 2023.
    30. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    31. Consolo, Agostino & Da Silva, António Dias, 2019. "The euro area labour market through the lens of the Beveridge curve," Economic Bulletin Articles, European Central Bank, vol. 4.
    32. Dany-Knedlik, Geraldine & Holtemöller, Oliver, 2017. "Inflation dynamics during the financial crisis in Europe: Cross-sectional identification of long-run inflation expectations," IWH Discussion Papers 10/2017, Halle Institute for Economic Research (IWH).
    33. 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.
    34. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    35. Chu Shiou-Yen & Shane Christopher, 2017. "Using the hybrid Phillips curve with memory to forecast US inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-16, September.
    36. Raïsa Basselier & David de Antonio Liedo & Jana Jonckheere & Geert Langenus, 2018. "Can inflation expectations in business or consumer surveys improve inflation forecasts?," Working Paper Research 348, National Bank of Belgium.
    37. 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.
    38. Michal Andrle & Miroslav Plasil, 2017. "System Priors for Econometric Time Series," Working Papers 2017/01, Czech National Bank.
    39. Mallick, Debdulal, 2014. "A Spectral Representation of the Phillips Curve in Australia," MPRA Paper 59794, University Library of Munich, Germany.
    40. 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.
    41. 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.
    42. Bowen Fu, Ivan Mendieta-Muñoz, 2023. "Structural shocks and trend inflation," Working Paper Series, Department of Economics, University of Utah 2023_04, University of Utah, Department of Economics.
    43. Grant, Angelia L., 2018. "The Great Recession and Okun's law," Economic Modelling, Elsevier, vol. 69(C), pages 291-300.
    44. Andrle, Michal & Plašil, Miroslav, 2018. "Econometrics with system priors," Economics Letters, Elsevier, vol. 172(C), pages 134-137.
    45. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  48. Tim J. Brereton & Dirk P. Kroese & Joshua C. Chan, 2012. "Monte Carlo Methods for Portfolio Credit Risk," ANU Working Papers in Economics and Econometrics 2012-579, Australian National University, College of Business and Economics, School of Economics.

    Cited by:

    1. Tang, Qihe & Tang, Zhaofeng & Yang, Yang, 2019. "Sharp asymptotics for large portfolio losses under extreme risks," European Journal of Operational Research, Elsevier, vol. 276(2), pages 710-722.
    2. Hatem Çoban & İpek Deveci Kocakoç & Şemsettin Erken & Mehmet Akif Aksoy, 2019. "Reducing Variation of Risk Estimation by Using Importance Sampling," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 7(2), pages 173-184, December.
    3. Ma, Yuan-Zhuo & Zhu, Yi-Chen & Li, Hong-Shuang & Nan, Hang & Zhao, Zhen-Zhou & Jin, Xiang-Xiang, 2022. "Adaptive Kriging-based failure probability estimation for multiple responses," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

  49. Joshua Chan & Rodney Strachan, 2012. "Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods," CAMA Working Papers 2012-13, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2016. "A Bounded Model of Time Variation in Trend Inflation, Nairu and the Phillips Curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 551-565, April.
    2. István Barra & Lennart Hoogerheide & Siem Jan Koopman & André Lucas, 2014. "Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models," Tinbergen Institute Discussion Papers 14-118/III, Tinbergen Institute, revised 31 Mar 2016.
    3. Andreza A Palma, 2016. "Natural interest rate in Brazil: further evidence frThe main objective of this study is to estimate the natural interest rate for Brazil using a parsimonious AR-trend-bound model proposed by Chan, Koo," Economics Bulletin, AccessEcon, vol. 36(3), pages 1306-1314.
    4. Eric Eisenstat & Joshua C.C. Chan & Rodney Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," Working Paper series 44_14, Rimini Centre for Economic Analysis.
    5. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    6. 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.
    7. Eric Eisenstat & Rodney Strachan, 2014. "Modelling Inflation Volatility," Working Paper series 43_14, Rimini Centre for Economic Analysis.
    8. Abhishek K. Umrawal & Joshua C. C. Chan, 2021. "On Parameter Estimation in Unobserved Components Models subject to Linear Inequality Constraints," Papers 2110.12149, arXiv.org, revised Feb 2023.
    9. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
    10. Dany-Knedlik, Geraldine & Holtemöller, Oliver, 2017. "Inflation dynamics during the financial crisis in Europe: Cross-sectional identification of long-run inflation expectations," IWH Discussion Papers 10/2017, Halle Institute for Economic Research (IWH).
    11. 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.

  50. Joshua C.C. Chan & Garry Koop & Roberto Leon Gonzales & Rodney W. Strachan, 2010. "Time Varying Dimension Models," ANU Working Papers in Economics and Econometrics 2010-523, Australian National University, College of Business and Economics, School of Economics.

    Cited by:

    1. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Jan Prüser, 2021. "Forecasting US inflation using Markov dimension switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 481-499, April.
    3. Joshua C.C. Chan, 2015. "The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling," CAMA Working Papers 2015-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Korobilis, Dimitris & Koop, Gary, 2020. "Bayesian dynamic variable selection in high dimensions," MPRA Paper 100164, University Library of Munich, Germany.
    5. Felix Abramovich & Vadim Grinshtein, 2013. "Estimation of a sparse group of sparse vectors," Biometrika, Biometrika Trust, vol. 100(2), pages 355-370.
    6. Gabriel Rodríguez & Renato Vassallo, 2022. "Time Evolution of External Shocks on Macroeconomic Fluctuations in Pacific Alliance Countries: Empirical Application using TVP-VAR-SV Models," Documentos de Trabajo / Working Papers 2022-508, Departamento de Economía - Pontificia Universidad Católica del Perú.
    7. Miguel, Belmonte & Gary, Koop, 2013. "Model Switching and Model Averaging in Time- Varying Parameter Regression Models," SIRE Discussion Papers 2013-34, Scottish Institute for Research in Economics (SIRE).
    8. Koop, Gary & Korobilis, Dimitris, 2012. "Large Time-Varying Parameter VARs," SIRE Discussion Papers 2012-14, Scottish Institute for Research in Economics (SIRE).
    9. Prüser, Jan, 2017. "Forecasting US inflation using Markov dimension switching," Ruhr Economic Papers 710, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    10. Koop, Gary & Korobilis, Dimitris, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," MPRA Paper 87972, University Library of Munich, Germany.
    11. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Paper 2019/2, Norges Bank.
    12. Pierre Guérin & Danilo Leiva-Leon, 2015. "Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data," Staff Working Papers 15-24, Bank of Canada.
    13. Florian Huber & Gary Koop & Michael Pfarrhofer, 2020. "Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations," Papers 2002.10274, arXiv.org.
    14. 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.
    15. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    16. Tino Berger & Gerdie Everaert & Hauke Vierke, 2015. "Testing for time variation in an unobserved components model for the U.S. economy," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 15/903, Ghent University, Faculty of Economics and Business Administration.
    17. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    18. Eric Eisenstat & Joshua C.C. Chan & Rodney Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," Working Paper series 44_14, Rimini Centre for Economic Analysis.
    19. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
    20. Joshua C.C. Chan & Eric Eisenstat, 2013. "Gibbs Samplers for VARMA and Its Extensions," ANU Working Papers in Economics and Econometrics 2013-604, Australian National University, College of Business and Economics, School of Economics.
    21. Dimitris Korobilis, 2019. "High-dimensional macroeconomic forecasting using message passing algorithms," Working Papers 2019_07, Business School - Economics, University of Glasgow.
    22. Dimitris Korobilis, 2010. "VAR Forecasting Using Bayesian Variable Selection," Working Paper series 51_10, Rimini Centre for Economic Analysis, revised Apr 2011.
    23. 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.
    24. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    25. Joshua C.C. Chan, 2013. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," CAMA Working Papers 2013-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    26. Fu, Bowen, 2020. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models," Economic Modelling, Elsevier, vol. 88(C), pages 320-340.
    27. Joshua C.C. Chan & Eric Eisenstat, 2018. "Comparing hybrid time-varying parameter VARs," CAMA Working Papers 2018-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    28. 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.
    29. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Exchange Rates under Model and Parameter Uncertainty," CQE Working Papers 3214, Center for Quantitative Economics (CQE), University of Muenster.
    30. 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.
    31. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    32. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    33. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    34. 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.
    35. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    36. Korobilis, D, 2017. "Forecasting with many predictors using message passing algorithms," Essex Finance Centre Working Papers 19565, University of Essex, Essex Business School.
    37. Takeshi Kimura & Jouchi Nakajima, 2013. "Identifying Conventional and Unconventional Monetary Policy Shocks: A Latent Threshold Approach," Bank of Japan Working Paper Series 13-E-7, Bank of Japan.
    38. Nonejad, Nima, 2023. "Modeling the out-of-sample predictive relationship between equity premium, returns on the price of crude oil and economic policy uncertainty using multivariate time-varying dimension models," Energy Economics, Elsevier, vol. 126(C).
    39. Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.
    40. 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.
    41. Luke Hartigan, 2015. "Changes in the Factor Structure of the U.S. Economy: Permanent Breaks or Business Cycle Regimes?," Discussion Papers 2015-17, School of Economics, The University of New South Wales.
    42. Hang Qian, 2014. "A Flexible State Space Model And Its Applications," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(2), pages 79-88, March.
    43. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    44. Prüser, Jan, 2023. "Data-based priors for vector error correction models," International Journal of Forecasting, Elsevier, vol. 39(1), pages 209-227.
    45. Ripamonti, Alexandre, 2013. "Rational Valuation Formula (RVF) and Time Variability in Asset Rates of Return," MPRA Paper 79460, University Library of Munich, Germany.
    46. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
    47. Rodríguez, Gabriel & Vassallo, Renato & Castillo B., Paul, 2023. "Effects of external shocks on macroeconomic fluctuations in Pacific Alliance countries," Economic Modelling, Elsevier, vol. 124(C).
    48. Beckmann, Joscha & Schüssler, Rainer, 2016. "Forecasting exchange rates under parameter and model uncertainty," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 267-288.
    49. Jordi Maas, 2014. "Forecasting inflation using time-varying Bayesian model averaging," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(3), pages 149-182, August.

Articles

  1. 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.
    See citations under working paper version above.
  2. 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.
    See citations under working paper version above.
  3. Joshua C. C. Chan, 2023. "Large Hybrid Time-Varying Parameter VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 890-905, July.
    See citations under working paper version above.
  4. 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).
    See citations under working paper version above.
  5. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    See citations under working paper version above.
  6. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    See citations under working paper version above.
  7. Chan, Joshua C.C. & Yu, Xuewen, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    See citations under working paper version above.
  8. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    See citations under working paper version above.
  9. Chan, Joshua C.C. & Santi, Caterina, 2021. "Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).

    Cited by:

    1. Nicole Branger & Mark Trede & Bernd Wilfling, 2024. "Extracting stock-market bubbles from dividend futures," CQE Working Papers 10724, Center for Quantitative Economics (CQE), University of Muenster.
    2. Andrey Shternshis & Piero Mazzarisi, 2024. "Variance of entropy for testing time-varying regimes with an application to meme stocks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 47(1), pages 215-258, June.

  10. 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.
    See citations under working paper version above.
  11. Joshua C. C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2020. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 692-711, September.
    See citations under working paper version above.
  12. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    See citations under working paper version above.
  13. Benati, Luca & Chan, Joshua & Eisenstat, Eric & Koop, Gary, 2020. "Identifying noise shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    See citations under working paper version above.
  14. 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.

    Cited by:

    1. Niko Hauzenberger & Florian Huber & Gary Koop, 2020. "Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods," Papers 2005.03906, arXiv.org, revised May 2023.
    2. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    3. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    4. Lin Liu, 2022. "Economic Uncertainty and Exchange Market Pressure: Evidence From China," SAGE Open, , vol. 12(1), pages 21582440211, January.
    5. Hai-Chuan Xu & Fredj Jawadi & Jie Zhou & Wei-Xing Zhou, 2023. "Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework," Empirical Economics, Springer, vol. 65(1), pages 93-110, July.
    6. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Bayesian Modeling of Time-Varying Parameters Using Regression Trees," Working Papers 23-05, Federal Reserve Bank of Cleveland.
    7. 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).
    8. Ding, Shusheng & Wang, Kaihao & Cui, Tianxiang & Du, Min, 2023. "The time-varying impact of geopolitical risk on natural resource prices: The post-COVID era evidence," Resources Policy, Elsevier, vol. 86(PB).
    9. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2022. "Bayesian Modeling of TVP-VARs Using Regression Trees," Papers 2209.11970, arXiv.org, revised May 2023.
    10. Kenwin Maung, 2021. "Estimating high-dimensional Markov-switching VARs," Papers 2107.12552, arXiv.org.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
    17. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    18. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.
    19. Giacomo Rella, 2021. "The Fed, housing and household debt over time," Department of Economics University of Siena 850, Department of Economics, University of Siena.
    20. 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.
    21. 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.
    22. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    23. Xu, Danyang & Hu, Yang & Corbet, Shaen & Lang, Chunlin, 2024. "Return connectedness of green bonds and financial investment channels in China: Implications for hedging and regulation," Research in International Business and Finance, Elsevier, vol. 70(PA).
    24. 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.
    25. 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.
    26. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    27. Song, Ying & Bouri, Elie & Ghosh, Sajal & Kanjilal, Kakali, 2021. "Rare earth and financial markets: Dynamics of return and volatility connectedness around the COVID-19 outbreak," Resources Policy, Elsevier, vol. 74(C).
    28. 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.
    29. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    30. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    31. Li, Shaoyu & Zhu, Chunhui & Shang, Yuhuang, 2023. "Hedging demand and near-zero swap spreads: Evidence from the Chinese interest rate swap market," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 170-185.
    32. 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.
    33. Li, Xixi & Yuan, Jingsong, 2024. "DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1123-1133.
    34. 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.
    35. Zhou, Dong-hai & Liu, Xiao-xing, 2023. "Do world stock markets “jump” together? A measure of high-frequency volatility risk spillover networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    36. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
    37. Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021. "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, vol. 101(C).
    38. Xu Gong & Yujing Jin & Chuanwang Sun, 2022. "Time‐varying pure contagion effect between energy and nonenergy commodity markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1960-1986, October.
    39. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    40. 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.
    41. Jeanne Terblanche & Dawie van Lill & Hylton Hollander, 2023. "Fiscal policy and dimensions of inequality in South Africa: A time-varying coefficient approach," Working Papers 05/2023, Stellenbosch University, Department of Economics.
    42. Vasilii Erokhin & Tianming Gao, 2020. "Impacts of COVID-19 on Trade and Economic Aspects of Food Security: Evidence from 45 Developing Countries," IJERPH, MDPI, vol. 17(16), pages 1-28, August.

  15. 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.
    See citations under working paper version above.
  16. Zhang, Bo & Chan, Joshua C.C. & Cross, Jamie L., 2020. "Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1318-1328.
    See citations under working paper version above.
  17. Chan Joshua C.C. & Fry-McKibbin Renée A. & Hsiao Cody Yu-Ling, 2019. "A regime switching skew-normal model of contagion," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(1), pages 1-24, February.

    Cited by:

    1. Peterson Owusu Junior & Imhotep Alagidede & George Tweneboah, 2020. "Shape-shift contagion in emerging markets equities: evidence from frequency- and time-domain analysis," Economics and Business Letters, Oviedo University Press, vol. 9(3), pages 146-156.
    2. Yu-Ling Hsiao, Cody & Wei, Xinyang & Sheng, Ni & Shao, Chengwu, 2021. "A joint test of policy contagion with application to the solar sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    3. Hsiao, Cody Yu-Ling & Chiu, Yi-Bin, 2024. "Financial contagion and networks among the oil and BRICS stock markets during seven episodes of crisis events," Journal of International Money and Finance, Elsevier, vol. 144(C).
    4. Hsiao, Cody Yu-Ling & Yang, Rui & Zheng, Xin & Chiu, Yi-Bin, 2023. "Evaluations of policy contagion for new energy vehicle industry in China," Energy Policy, Elsevier, vol. 173(C).
    5. Aboagye, Ernest & Ko, Stanley Iat-Meng & Lo, Chia Chun & Hsiao, Cody Yu-Ling & Peng, Liang, 2024. "A contagion test with unspecified heteroscedastic errors," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    6. 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.
    7. Cody Yu-Ling Hsiao & James Morley, 2022. "Debt and financial market contagion," Empirical Economics, Springer, vol. 62(4), pages 1599-1648, April.
    8. Renée Fry-McKibbin & Cody Yu-Ling Hsiao & Vance L. Martin, 2018. "Measuring financial interdependence in asset returns with an application to euro zone equities," CAMA Working Papers 2018-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Roman Matkovskyy & Akanksha Jalan, 2019. "From financial markets to Bitcoin markets: A fresh look at the contagion effect," Post-Print hal-02131637, HAL.
    10. Zhang, Qun & Zhang, Zhendong & Luo, Jiawen, 2024. "Asymmetric and high-order risk transmission across VIX and Chinese futures markets," International Review of Financial Analysis, Elsevier, vol. 93(C).

  18. Joshua Chan & Roberto Leon-Gonzalez & Rodney W. Strachan, 2018. "Invariant Inference and Efficient Computation in the Static Factor Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 819-828, April.
    See citations under working paper version above.
  19. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    See citations under working paper version above.
  20. Chan, Joshua C.C. & Eisenstat, Eric, 2018. "Comparing hybrid time-varying parameter VARs," Economics Letters, Elsevier, vol. 171(C), pages 1-5.
    See citations under working paper version above.
  21. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    See citations under working paper version above.
  22. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    See citations under working paper version above.
  23. Joshua C.C. Chan & Yong Song, 2018. "Measuring Inflation Expectations Uncertainty Using High‐Frequency Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(6), pages 1139-1166, September.
    See citations under working paper version above.
  24. Joshua C.C. Chan & Eric Eisenstat, 2017. "Efficient estimation of Bayesian VARMAs with time†varying coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1277-1297, November.

    Cited by:

    1. Mehmet Balcilar & Edmond Berisha & Oguzhan Cepni & Rangan Gupta, 2019. "The Predictive Power of the Term Spread on Inequality in the United Kingdom: An Empirical Analysis," Working Papers 201981, University of Pretoria, Department of Economics.
    2. Benati, Luca & Chan, Joshua & Eisenstat, Eric & Koop, Gary, 2020. "Identifying noise shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    3. Oguzhan Cepni & David Gabauer & Rangan Gupta & Khuliso Ramabulana, 2020. "Time-Varying Spillover of US Trade War on the Growth of Emerging Economies," Working Papers 202002, University of Pretoria, Department of Economics.
    4. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    5. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.

  25. Grant, Angelia L. & Chan, Joshua C.C., 2017. "Reconciling output gaps: Unobserved components model and Hodrick–Prescott filter," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 114-121.
    See citations under working paper version above.
  26. Joshua C. C. Chan, 2017. "The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 17-28, January.
    See citations under working paper version above.
  27. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    See citations under working paper version above.
  28. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    See citations under working paper version above.
  29. Eric Eisenstat & Joshua C. C. Chan & Rodney W. Strachan, 2016. "Stochastic Model Specification Search for Time-Varying Parameter VARs," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1638-1665, December.
    See citations under working paper version above.
  30. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Modeling energy price dynamics: GARCH versus stochastic volatility," Energy Economics, Elsevier, vol. 54(C), pages 182-189.
    See citations under working paper version above.
  31. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2016. "Large Bayesian VARMAs," Journal of Econometrics, Elsevier, vol. 192(2), pages 374-390.
    See citations under working paper version above.
  32. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2016. "A Bounded Model of Time Variation in Trend Inflation, Nairu and the Phillips Curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 551-565, April.
    See citations under working paper version above.
  33. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 772-802.

    Cited by:

    1. Dominik Bertsche & Robin Braun, 2018. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Working Paper Series of the Department of Economics, University of Konstanz 2018-03, Department of Economics, University of Konstanz.
    2. Martin Iseringhausen, 2018. "The Time-Varying Asymmetry Of Exchange Rate Returns: A Stochastic Volatility – Stochastic Skewness Model," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 18/944, Ghent University, Faculty of Economics and Business Administration.
    3. Wang, Renhe & Wang, Tong & Qian, Zhiyong & Hu, Shulan, 2023. "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast," Finance Research Letters, Elsevier, vol. 58(PC).
    4. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    5. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    6. Baum, Christopher F. & Zerilli, Paola & Chen, Liyuan, 2021. "Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data," Energy Economics, Elsevier, vol. 93(C).
    7. Nima Nonejad, 2020. "Reproducing the results in “Does the time-consistency problem explain the behavior of inflation in the United States?” using the Metropolis–Hastings algorithm," Empirical Economics, Springer, vol. 59(5), pages 2559-2571, November.
    8. Aknouche Abdelhakim & Demmouche Nacer & Dimitrakopoulos Stefanos & Touche Nassim, 2020. "Bayesian analysis of periodic asymmetric power GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-24, September.
    9. Doğan, Osman, 2023. "Modified harmonic mean method for spatial autoregressive models," Economics Letters, Elsevier, vol. 223(C).
    10. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2017. "Cohort effects in mortality modelling: a Bayesian state-space approach," Papers 1703.08282, arXiv.org.
    11. 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).
    12. Zhongxian Men & Adam W. Kolkiewicz & Tony S. Wirjanto, 2019. "Threshold Stochastic Conditional Duration Model for Financial Transaction Data," JRFM, MDPI, vol. 12(2), pages 1-21, May.
    13. Florian Huber & Michael Pfarrhofer, 2020. "Dynamic shrinkage in time-varying parameter stochastic volatility in mean models," Papers 2005.06851, arXiv.org.
    14. Chen, Rongda & Bao, Weiwei & Jin, Chenglu, 2021. "Investor sentiment and predictability for volatility on energy futures Markets: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 112-129.
    15. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    16. Heejoon Han & Eunhee Lee, 2020. "Triple Regime Stochastic Volatility Model with Threshold and Leverage Effects," Korean Economic Review, Korean Economic Association, vol. 36, pages 481-509.
    17. Flavio Pérez Rojo & Gabriel Rodríguez, 2023. "Jane Haldimand Marcet: Impact of Monetary Policy Shocks in the Peruvian Economy Over Time," Documentos de Trabajo / Working Papers 2023-523, Departamento de Economía - Pontificia Universidad Católica del Perú.
    18. Ye Yang & Osman Dogan & Suleyman Taspinar & Fei Jin, 2023. "A Review of Cross-Sectional Matrix Exponential Spatial Models," Papers 2311.14813, arXiv.org.
    19. Jaeho Kim & Sora Chon, 2022. "Bayesian estimation of the long-run trend of the US economy," Empirical Economics, Springer, vol. 62(2), pages 461-485, February.
    20. 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.
    21. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2018. "High-frequency Cash Flow Dynamics," Working Papers 120, Brandeis University, Department of Economics and International Business School.
    22. Daniel J Lewis, 2021. "Identifying Shocks via Time-Varying Volatility [First Order Autoregressive Processes and Strong Mixing]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 3086-3124.
    23. Nonejad, Nima, 2017. "Parameter instability, stochastic volatility and estimation based on simulated likelihood: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 61(C), pages 388-408.
    24. Wilson Ye Chen & Richard H. Gerlach, 2017. "Semiparametric GARCH via Bayesian model averaging," Papers 1708.07587, arXiv.org.
    25. 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.
    26. Nima Nonejad, 2019. "Modeling Persistence and Parameter Instability in Historical Crude Oil Price Data Using a Gibbs Sampling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1687-1710, April.
    27. George P. Papaioannou & Christos Dikaiakos & Akylas C. Stratigakos & Panos C. Papageorgiou & Konstantinos F. Krommydas, 2019. "Testing the Efficiency of Electricity Markets Using a New Composite Measure Based on Nonlinear TS Tools," Energies, MDPI, vol. 12(4), pages 1-30, February.
    28. Yoshihiro Ohtsuka, 2018. "Large Shocks and the Business Cycle: The Effect of Outlier Adjustments," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 143-178, April.
    29. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    30. Oludare Samuel Ariyo & Matthew Adekunle Adeleke, 2022. "Simultaneous Bayesian modelling of skew-normal longitudinal measurements with non-ignorable dropout," Computational Statistics, Springer, vol. 37(1), pages 303-325, March.
    31. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.
    32. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2018. "Does time-variation matter in the stochastic volatility components for G7 stock returns," Working Papers 062, Centre for Econometric and Allied Research, University of Ibadan.
    33. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    34. 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).
    35. Fedele Greco & Carlo Trivisano, 2018. "Comments on: Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 549-553, September.
    36. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    37. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Martijn Huisman & Martijn Heymans & Jos Twisk, 2022. "Bayesian model selection for multilevel mediation models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 219-235, May.
    38. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.
    39. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    40. Daniel J. Lewis, 2019. "Announcement-Specific Decompositions of Unconventional Monetary Policy Shocks and Their Macroeconomic Effects," Staff Reports 891, Federal Reserve Bank of New York.
    41. Mário Correia Fernandes & José Carlos Dias & João Pedro Vidal Nunes, 2024. "Performance comparison of alternative stochastic volatility models and its determinants in energy futures: COVID‐19 and Russia–Ukraine conflict features," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(3), pages 343-383, March.
    42. Chon, Sora & Kim, Jaeho, 2021. "Does the Financial Leverage Effect Depend on Volatility Regimes?," Finance Research Letters, Elsevier, vol. 39(C).
    43. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Adrian Quintero, 2022. "Bayesian Model Selection for Longitudinal Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 516-547, November.
    44. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.
    45. Dante A. Urbina & Gabriel Rodríguez, 2023. "Evolution of the effects of mineral commodity prices on fiscal fluctuations: empirical evidence from TVP-VAR-SV models for Peru," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 159(1), pages 153-184, February.

  34. Joshua C. C. Chan & Eric Eisenstat, 2015. "Marginal Likelihood Estimation with the Cross-Entropy Method," Econometric Reviews, Taylor & Francis Journals, vol. 34(3), pages 256-285, March.
    See citations under working paper version above.
  35. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    See citations under working paper version above.
  36. Joshua C. C. Chan & Justin L. Tobias, 2015. "Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 650-674, June.
    See citations under working paper version above.
  37. Chan, Joshua C.C. & Koop, Gary, 2014. "Modelling breaks and clusters in the steady states of macroeconomic variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 186-193.
    See citations under working paper version above.
  38. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    See citations under working paper version above.
  39. Chan, Joshua C.C., 2013. "Moving average stochastic volatility models with application to inflation forecast," Journal of Econometrics, Elsevier, vol. 176(2), pages 162-172.
    See citations under working paper version above.
  40. Joshua C.C. Chan & Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2012. "Time Varying Dimension Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 358-367, January.
    See citations under working paper version above.
  41. Joshua Chan & Dirk Kroese, 2011. "Rare-event probability estimation with conditional Monte Carlo," Annals of Operations Research, Springer, vol. 189(1), pages 43-61, September.

    Cited by:

    1. Mohamed A. Ayadi & Hatem Ben-Ameur & Nabil Channouf & Quang Khoi Tran, 2019. "NORTA for portfolio credit risk," Annals of Operations Research, Springer, vol. 281(1), pages 99-119, October.
    2. Loretta Mastroeni & Giuseppe D'Acquisto & Maurizio Naldi, 2014. "Evaluation of Credit Risk Under Correlated Defaults: The Cross-Entropy Simulation Approach," Departmental Working Papers of Economics - University 'Roma Tre' 0193, Department of Economics - University Roma Tre.
    3. Ping-Chen Chang, 2019. "Reliability estimation for a stochastic production system with finite buffer storage by a simulation approach," Annals of Operations Research, Springer, vol. 277(1), pages 119-133, June.
    4. Cao, Quoc Dung & Choe, Youngjun, 2019. "Cross-entropy based importance sampling for stochastic simulation models," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    5. Hansjörg Albrecher & Martin Bladt & Eleni Vatamidou, 2021. "Efficient Simulation of Ruin Probabilities When Claims are Mixtures of Heavy and Light Tails," Methodology and Computing in Applied Probability, Springer, vol. 23(4), pages 1237-1255, December.
    6. Hélène Cossette & Etienne Marceau & Quang Huy Nguyen & Christian Y. Robert, 2019. "Tail Approximations for Sums of Dependent Regularly Varying Random Variables Under Archimedean Copula Models," Methodology and Computing in Applied Probability, Springer, vol. 21(2), pages 461-490, June.
    7. Sak Halis, 2010. "Increasing the number of inner replications of multifactor portfolio credit risk simulation in the t-copula model," Monte Carlo Methods and Applications, De Gruyter, vol. 16(3-4), pages 361-377, January.

  42. Chan, Joshua C.C. & Kroese, Dirk P., 2010. "Efficient estimation of large portfolio loss probabilities in t-copula models," European Journal of Operational Research, Elsevier, vol. 205(2), pages 361-367, September.

    Cited by:

    1. Balaev, Alexey, 2014. "The copula based on multivariate t-distribution with vector of degrees of freedom," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 90-110.
    2. Mohamed A. Ayadi & Hatem Ben-Ameur & Nabil Channouf & Quang Khoi Tran, 2019. "NORTA for portfolio credit risk," Annals of Operations Research, Springer, vol. 281(1), pages 99-119, October.
    3. Joshua C C Chan & Eric Eisenstat, 2012. "Marginal Likelihood Estimation with the Cross-Entropy Method," CAMA Working Papers 2012-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Gong, Xiao-Li & Xiong, Xiong, 2021. "Multi-objective portfolio optimization under tempered stable Lévy distribution with Copula dependence," Finance Research Letters, Elsevier, vol. 38(C).
    5. Erik Hintz & Marius Hofert & Christiane Lemieux & Yoshihiro Taniguchi, 2022. "Single-Index Importance Sampling with Stratification," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 3049-3073, December.
    6. Sunggon Kim & Jisu Yu, 2023. "Stratified importance sampling for a Bernoulli mixture model of portfolio credit risk," Annals of Operations Research, Springer, vol. 322(2), pages 819-849, March.
    7. Millar, Robert & Li, Hui & Li, Jinglai, 2023. "Multicanonical sequential Monte Carlo sampler for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. D’Amico, Guglielmo & Petroni, Filippo, 2018. "Copula based multivariate semi-Markov models with applications in high-frequency finance," European Journal of Operational Research, Elsevier, vol. 267(2), pages 765-777.
    9. Laih, Yih-Wenn, 2014. "Measuring rank correlation coefficients between financial time series: A GARCH-copula based sequence alignment algorithm," European Journal of Operational Research, Elsevier, vol. 232(2), pages 375-382.
    10. Huang, Zhenzhen & Kwok, Yue Kuen & Xu, Ziqing, 2024. "Efficient algorithms for calculating risk measures and risk contributions in copula credit risk models," Insurance: Mathematics and Economics, Elsevier, vol. 115(C), pages 132-150.
    11. Wolter, Marcus & Rösch, Daniel, 2014. "Cure events in default prediction," European Journal of Operational Research, Elsevier, vol. 238(3), pages 846-857.
    12. Cheng-Der Fuh & Chuan-Ju Wang, 2017. "Efficient Exponential Tilting for Portfolio Credit Risk," Papers 1711.03744, arXiv.org, revised Apr 2019.
    13. Dimitrov, Daniel & van Wijnbergen, Sweder, 2023. "Quantifying Systemic Risk in the Presence of Unlisted Banks: Application to the European Banking Sector," CEPR Discussion Papers 17992, C.E.P.R. Discussion Papers.
    14. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
    15. Tang, Qihe & Tang, Zhaofeng & Yang, Yang, 2019. "Sharp asymptotics for large portfolio losses under extreme risks," European Journal of Operational Research, Elsevier, vol. 276(2), pages 710-722.
    16. Rongda Chen & Ze Wang & Lean Yu, 2017. "Importance Sampling for Credit Portfolio Risk with Risk Factors Having t-Copula," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1101-1124, July.
    17. Michael Stanley Smith, 2021. "Implicit Copulas: An Overview," Papers 2109.04718, arXiv.org.
    18. Indranil Ghosh & Dalton Watts & Subrata Chakraborty, 2022. "Modeling Bivariate Dependency in Insurance Data via Copula: A Brief Study," JRFM, MDPI, vol. 15(8), pages 1-20, July.
    19. Lützenkirchen, Kristina & Rösch, Daniel & Scheule, Harald, 2014. "Asset portfolio securitizations and cyclicality of regulatory capital," European Journal of Operational Research, Elsevier, vol. 237(1), pages 289-302.
    20. Kakouris, Iakovos & Rustem, Berç, 2014. "Robust portfolio optimization with copulas," European Journal of Operational Research, Elsevier, vol. 235(1), pages 28-37.
    21. Adam Metzler & Alexandre Scott, 2020. "Importance Sampling in the Presence of PD-LGD Correlation," Risks, MDPI, vol. 8(1), pages 1-36, March.
    22. Ferrer, Alex & Casals, José & Sotoca, Sonia, 2016. "Efficient estimation of unconditional capital by Monte Carlo simulation," Finance Research Letters, Elsevier, vol. 16(C), pages 75-84.
    23. Tahani S. Alotaibi & Luciana Dalla Valle & Matthew J. Craven, 2022. "The Worst Case GARCH-Copula CVaR Approach for Portfolio Optimisation: Evidence from Financial Markets," JRFM, MDPI, vol. 15(10), pages 1-14, October.
    24. Hélène Cossette & Etienne Marceau & Quang Huy Nguyen & Christian Y. Robert, 2019. "Tail Approximations for Sums of Dependent Regularly Varying Random Variables Under Archimedean Copula Models," Methodology and Computing in Applied Probability, Springer, vol. 21(2), pages 461-490, June.
    25. Fenech, Jean Pierre & Vosgha, Hamed & Shafik, Salwa, 2015. "Loan default correlation using an Archimedean copula approach: A case for recalibration," Economic Modelling, Elsevier, vol. 47(C), pages 340-354.
    26. Bai, Zhidong & Li, Hua & Wong, Wing-Keung, 2013. "The best estimation for high-dimensional Markowitz mean-variance optimization," MPRA Paper 43862, University Library of Munich, Germany.
    27. Tang, Qihe & Tong, Zhiwei & Yang, Yang, 2021. "Large portfolio losses in a turbulent market," European Journal of Operational Research, Elsevier, vol. 292(2), pages 755-769.
    28. Hengxin Cui & Ken Seng Tan & Fan Yang, 2024. "Portfolio credit risk with Archimedean copulas: asymptotic analysis and efficient simulation," Papers 2411.06640, arXiv.org.
    29. İsmail Başoğlu & Wolfgang Hörmann & Halis Sak, 2018. "Efficient simulations for a Bernoulli mixture model of portfolio credit risk," Annals of Operations Research, Springer, vol. 260(1), pages 113-128, January.
    30. Tim J. Brereton & Dirk P. Kroese & Joshua C. Chan, 2012. "Monte Carlo Methods for Portfolio Credit Risk," ANU Working Papers in Economics and Econometrics 2012-579, Australian National University, College of Business and Economics, School of Economics.
    31. Sahar Abbas & Fahimeh Moosavi, 2012. "Finding shortest path in static networks: using a modified algorithm," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 1(1), pages 29-34, January.
    32. Scott, Alexandre & Metzler, Adam, 2015. "A general importance sampling algorithm for estimating portfolio loss probabilities in linear factor models," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 279-293.
    33. Ali Kadhem, Athraa & Abdul Wahab, Noor Izzri & Aris, Ishak & Jasni, Jasronita & Abdalla, Ahmed N., 2017. "Computational techniques for assessing the reliability and sustainability of electrical power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1175-1186.
    34. Chu, Ba & Knight, John & Satchell, Stephen, 2011. "Large deviations theorems for optimal investment problems with large portfolios," European Journal of Operational Research, Elsevier, vol. 211(3), pages 533-555, June.
    35. Leung, Pui-Lam & Ng, Hon-Yip & Wong, Wing-Keung, 2012. "An improved estimation to make Markowitz’s portfolio optimization theory users friendly and estimation accurate with application on the US stock market investment," European Journal of Operational Research, Elsevier, vol. 222(1), pages 85-95.
    36. Gregor Wei{ss} & Marcus Scheffer, 2012. "Smooth Nonparametric Bernstein Vine Copulas," Papers 1210.2043, arXiv.org.

Chapters

  1. Justin L. Tobias & Joshua C. C. Chan, 2019. "An Alternate Parameterization for Bayesian Nonparametric/Semiparametric Regression," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 47-64, Emerald Group Publishing Limited.

    Cited by:

    1. Murat K. Munkin, 2022. "Count Roy model with finite mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1160-1181, September.

  2. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2019. "How Sensitive Are VAR Forecasts to Prior Hyperparameters? An Automated Sensitivity Analysis," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 229-248, Emerald Group Publishing Limited.
    See citations under working paper version above.

Books

  1. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108423380, January.

    Cited by:

    1. Olivier Parent & James P. Lesage, 2010. "A Spatial Dynamic Panel Model with Random Effects Applied to Commuting Times," University of Cincinnati, Economics Working Papers Series 2010-01, University of Cincinnati, Department of Economics.
    2. Ahtiainen, Heini & Vanhatalo, Jarno, 2012. "The value of reducing eutrophication in European marine areas — A Bayesian meta-analysis," Ecological Economics, Elsevier, vol. 83(C), pages 1-10.
    3. Haider, Adnan & Khan, Safdar Ullah, 2008. "A Small Open Economy DSGE Model for Pakistan," MPRA Paper 12977, University Library of Munich, Germany, revised 17 Jan 2009.
    4. Huber, Florian & Onorante, Luca & Pfarrhofer, Michael, 2024. "Forecasting euro area inflation using a huge panel of survey expectations," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1042-1054.
    5. Abidoye, Babatunde O. & Herriges, Joseph A. & Tobias, Justin L., 2010. "Controlling for observed and unobserved site characteristics in RUM models of recreation demand," ISU General Staff Papers 201005250700001115, Iowa State University, Department of Economics.
    6. Colson, Gregory & Rousu, Matthew C. & Huffman, Wallace E., 2008. "Consumers' Willingness to Pay for New Genetically Modified Food Products: Evidence from Experimental Auctions of Intragenic and Transgenic Foods," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6407, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. Danaf, Mazen & Guevara, C. Angelo & Ben-Akiva, Moshe, 2023. "A control-function correction for endogeneity in random coefficients models: The case of choice-based recommender systems," Journal of choice modelling, Elsevier, vol. 46(C).
    8. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    9. Gardebroek, Cornelis & Oude Lansink, Alfons G.J.M., 2008. "Dynamic Microeconometric Approaches To Analysing Agricultural Policy," 107th Seminar, January 30-February 1, 2008, Sevilla, Spain 6592, European Association of Agricultural Economists.
    10. Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.
    11. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    12. 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).
    13. C. Glocker & G. Sestieri & P. Towbin, 2017. "Time-varying fiscal spending multipliers in the UK," Working papers 643, Banque de France.
    14. Florian Huber & Gary Koop, 2021. "Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions," Papers 2107.07804, arXiv.org.
    15. Ishdorj, Ariun & Jensen, Helen H., 2010. "Demand For Breakfast Cereals: Whole Grains Guidance And Food Choice," 115th Joint EAAE/AAEA Seminar, September 15-17, 2010, Freising-Weihenstephan, Germany 116445, European Association of Agricultural Economists.
    16. Mengheng Li & Marcel Scharth, 2022. "Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochastic Volatility Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 285-301, January.
    17. Danilo Leiva-Leon & Luis Uzeda, 2020. "Endogenous Time Variation in Vector Autoregressions," Staff Working Papers 20-16, Bank of Canada.
    18. Daeseok Han, 2021. "Heterogeneous Deterioration Process and Risk of Deficiencies of Aging Bridges for Transportation Asset Management," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    19. Bernardi Mauro & Della Corte Giuseppe & Proietti Tommaso, 2011. "Extracting the Cyclical Component in Hours Worked," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-28, May.
    20. Richard S. J. Tol & In Chang Hwang & Frédéric Reynès, 2012. "The Effect of Learning on Climate Policy under Fat-tailed Uncertainty," Working Paper Series 5312, Department of Economics, University of Sussex Business School.
    21. Caio Waisman & Harikesh S. Nair & Carlos Carrion, 2019. "Online Causal Inference for Advertising in Real-Time Bidding Auctions," Papers 1908.08600, arXiv.org, revised Feb 2024.
    22. Koenig, Michael & Hsieh, Chih-Sheng & Liu, Xiaodong & Zimmermann, Christian, 2018. "Superstar Economists: Coauthorship networks and research output," CEPR Discussion Papers 13239, C.E.P.R. Discussion Papers.
    23. Helmut Herwartz & Christian Ochsner & Hannes Rohloff, 2021. "The Credit Composition of Global Liquidity," MAGKS Papers on Economics 202115, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    24. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    25. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    26. 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.
    27. Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin, 2008. "Estimating Demand Systems when Outcomes Are Correlated Count," Staff General Research Papers Archive 12934, Iowa State University, Department of Economics.
    28. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    29. Xiaodong Du & David A. Hennessy & Cindy L. Yu, 2012. "Testing Day's Conjecture that More Nitrogen Decreases Crop Yield Skewness," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(1), pages 225-237.
    30. Christopher N. Boyer & Dayton M. Lambert & Charles C. Martinez & Joshua G. Maples, 2023. "Beef and pork processing plant labor costs," Agribusiness, John Wiley & Sons, Ltd., vol. 39(3), pages 691-702, July.
    31. Roman Horváth, 2012. "Does Trust Promote Growth?," Working Papers 319, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
    32. Abidoye, Babatunde O. & Marlene, Labuschagne, 2012. "The Transmission of World Maize Price to South African Maize Market: A Threshold Cointegration Approach," Working Papers 206515, University of Pretoria, Department of Agricultural Economics, Extension and Rural Development.
    33. Rob Luginbuhl, 2020. "Estimation of the Financial Cycle with a Rank-Reduced Multivariate State-Space Model," CPB Discussion Paper 409, CPB Netherlands Bureau for Economic Policy Analysis.
    34. Parley Ruogu Yang, 2021. "Forecasting high-frequency financial time series: an adaptive learning approach with the order book data," Papers 2103.00264, arXiv.org.
    35. Jed Cohen, Klaus Moeltner, Johannes Reichl and Michael Schmidthaler, 2016. "An Empirical Analysis of Local Opposition to New Transmission Lines Across the EU-27," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    36. Mountford, Andrew, 2024. "Economic Growth Analysis When Balanced Growth Paths May Be Time Varying," MPRA Paper 119938, University Library of Munich, Germany.
    37. Iftekhar Hasan & Roman Horvath & Jan Mares, 2018. "Finance and Wealth Inequality," Working Papers IES 2018/35, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Dec 2018.
    38. Dogan, Osman & Taspinar, Suleyman, 2016. "Bayesian Inference in Spatial Sample Selection Models," MPRA Paper 82829, University Library of Munich, Germany.
    39. Ryosuke Igari & Takahiro Hoshino, 2017. "Bayesian Data Combination Approach for Repeated Durations under Unobserved Missing Indicators: Application to Interpurchase-Timing in Marketing," Keio-IES Discussion Paper Series 2017-015, Institute for Economics Studies, Keio University.
    40. Du, Xiaodong & Carriquiry, Miguel A., 2013. "Spatiotemporal analysis of ethanol market penetration," Energy Economics, Elsevier, vol. 38(C), pages 128-135.
    41. Eaton, Derek J.F., 2009. "Trade and Intellectual Property Rights in the Agricultural Seed Sector," 2009 Conference, August 16-22, 2009, Beijing, China 51782, International Association of Agricultural Economists.
    42. Ahn, Hie Joo, 2023. "Duration structure of unemployment hazards and the trend unemployment rate," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    43. Peter Lenk, 2014. "Bayesian estimation of random utility models," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 20, pages 457-497, Edward Elgar Publishing.
    44. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Anne Opschoor & Herman K. van Dijk, 2015. "The R-package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Tinbergen Institute Discussion Papers 15-042/III, Tinbergen Institute, revised 04 Jul 2017.
    45. Roman Horvath & Marek Rusnak & Katerina Smidkova & Jan Zapal, 2014. "The dissent voting behaviour of central bankers: what do we really know?," Applied Economics, Taylor & Francis Journals, vol. 46(4), pages 450-461, February.
    46. Doğan, Osman & Taşpınar, Süleyman, 2014. "Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 1-21.
    47. Colson, Gregory J. & Huffman, Wallace E. & Rousu, Matthew C., 2011. "Improving the Nutrient Content of Food through Genetic Modification: Evidence from Experimental Auctions on Consumer Acceptance," ISU General Staff Papers 201108010700001371, Iowa State University, Department of Economics.
    48. Cerrato, Mario & Crosby, John & Kaleem, Muhammad, 2011. "Measuring the Economic Significance of Structural Exchange Rate Models," SIRE Discussion Papers 2011-62, Scottish Institute for Research in Economics (SIRE).
    49. Guangjie Li, 2015. "Consistency in Estimation and Model Selection of Dynamic Panel Data Models with Fixed Effects," Econometrics, MDPI, vol. 3(3), pages 1-31, July.
    50. Chih-Sheng Hsieh & Michael D. König & Xiaodong Liu & Christian Zimmermann, 2020. "Collaboration in Bipartite Networks, with an Application to Coauthorship Networks," Working Papers 2020-030, Federal Reserve Bank of St. Louis.
    51. Ahlem DAHEM, 2016. "Short-Term Bayesian Inflation Forecasting For Tunisia: Some Empirical Evidence," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 5(1), pages 1-47, January.
    52. Knut Are Aastveit & Gisle James Natvik & Sergio Sola, 2013. "Economic uncertainty and the effectiveness of monetary policy," Working Paper 2013/17, Norges Bank.
    53. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    54. Katharina Hauck & Xiaohui Zhang, 2016. "Heterogeneity in the Effect of Common Shocks on Healthcare Expenditure Growth," Health Economics, John Wiley & Sons, Ltd., vol. 25(9), pages 1090-1103, September.
    55. Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, arXiv.org.
    56. Moukam, Claudiane Yanick & Atewamba, Calvin, 2023. "Incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural Cameroon," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 12(3), October.
    57. Horvath, Roman, 2011. "Research & development and growth: A Bayesian model averaging analysis," Economic Modelling, Elsevier, vol. 28(6), pages 2669-2673.
    58. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    59. Robin C. Sickles & Jiaqi Hao & Chenjun Shang, 2014. "Panel data and productivity measurement: an analysis of Asian productivity trends," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 12(3), pages 211-231, August.
    60. Nikolaus Hautsch & Fuyu Yang, 2014. "Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth," University of East Anglia Applied and Financial Economics Working Paper Series 056, School of Economics, University of East Anglia, Norwich, UK..
    61. Danaf, Mazen & Guevara, Angelo & Atasoy, Bilge & Ben-Akiva, Moshe, 2020. "Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys," Journal of choice modelling, Elsevier, vol. 34(C).
    62. Haider, Adnan & Jan, Asad & Hyder, Kalim, 2012. "On the (IR) Relevance of Monetary Aggregate Targeting in Pakistan: An Eclectic View," MPRA Paper 43422, University Library of Munich, Germany.
    63. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    64. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    65. Abidoye, Babatunde Oluwakayode, 2010. "Bayesian inference in modeling recreation demand," ISU General Staff Papers 201001010800002496, Iowa State University, Department of Economics.
    66. Herriges, Joseph & Kling, Catherine L. & Liu, Chih-Chen & Tobias, Justin, 2010. "What are the consequences of consequentiality?," ISU General Staff Papers 201001010800001561, Iowa State University, Department of Economics.
    67. Hong Wang & Catherine S. Forbes & Jean-Pierre Fenech & John Vaz, 2018. "The determinants of bank loan recovery rates in good times and bad - new evidence," Papers 1804.07022, arXiv.org.
    68. S. Standaert, 2013. "Divining the Level of Corruption. A Bayesian State-Space Approach," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/835, Ghent University, Faculty of Economics and Business Administration.
    69. Mohammad Arshad Rahman & Angela Vossmeyer, 2019. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, volume 40, pages 157-191, Emerald Group Publishing Limited.
    70. Saqib Amin & Ruhamah Yousaf & Muhammad Awais Anwar & Noman Arshed, 2022. "Assessing the impact of diversity and ageing population on health expenditure of United States," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(2), pages 913-929, March.
    71. Chih-Sheng Hsieh & Michael D König & Xiaodong Liu & Christian Zimmermann, 2022. "Collaboration in Bipartite Networks," Working Papers 2202, National Taiwan University, Department of Economics, revised Feb 2022.
    72. Koenig, Michael & Hsieh, Chih-Sheng & Liu, Xiaodong, 2018. "Network Formation with Local Complements and Global Substitutes: The Case of R&D Networks," CEPR Discussion Papers 13161, C.E.P.R. Discussion Papers.
    73. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    74. Li, Mingliang & Tobias, Justin L., 2011. "Bayesian inference in a correlated random coefficients model: Modeling causal effect heterogeneity with an application to heterogeneous returns to schooling," Journal of Econometrics, Elsevier, vol. 162(2), pages 345-361, June.
    75. Li, Phillip, 2010. "Estimation of Sample Selection Models With Two Selection Mechanisms," University of California Transportation Center, Working Papers qt0h97w9x2, University of California Transportation Center.
    76. Jiaying Deng & Hossein Ghasemkhani & Yong Tan & Arvind K Tripathi, 2023. "Actions speak louder than words: Imputing users’ reputation from transaction history," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1096-1111, April.
    77. Eicher, Theo S. & Helfman, Lindy & Lenkoski, Alex, 2012. "Robust FDI determinants: Bayesian Model Averaging in the presence of selection bias," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 637-651.
    78. Daeseok Han & Jin-Hyuk Lee & Ki-Tae Park, 2022. "Deterioration Models for Bridge Pavement Materials for a Life Cycle Cost Analysis," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    79. 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.
    80. Moeltner, Klaus & Blinn, Christine E. & Holmes, Thomas P., 2017. "Forest pests and home values: The importance of accuracy in damage assessment and geocoding of properties," Journal of Forest Economics, Elsevier, vol. 26(C), pages 46-55.
    81. Adriana Nikolic & Christoph Weiss, 2014. "Spatial interactions in location decisions: Empirical evidence from a Bayesian spatial probit model," Department of Economics Working Papers wuwp177, Vienna University of Economics and Business, Department of Economics.
    82. Dahem, Ahlem, 2015. "Short term Bayesian inflation forecasting for Tunisia," MPRA Paper 66702, University Library of Munich, Germany.
    83. Han, Xiaoyi & Hsieh, Chih-Sheng & Lee, Lung-fei, 2017. "Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 97-120.
    84. Baltagi, Badi H. & Bresson, Georges & Chaturvedi, Anoop & Lacroix, Guy, 2022. "Robust Dynamic Space-Time Panel Data Models Using ?-Contamination: An Application to Crop Yields and Climate Change," IZA Discussion Papers 15815, Institute of Labor Economics (IZA).
    85. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    86. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    87. 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.
    88. Mihaela Simionescu (Bratu), 2014. "The Bayesian Modelling Of Inflation Rate In Romania," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 147-160, June.
    89. Maeng, Kyuho & Jeon, Seung Ryong & Park, Taeho & Cho, Youngsang, 2021. "Network effects of connected and autonomous vehicles in South Korea: A consumer preference approach," Research in Transportation Economics, Elsevier, vol. 90(C).
    90. Maria Chiara D'Errico & Carlo Andrea Bollino, 2015. "Bayesian Analysis of Demand Elasticity in the Italian Electricity Market," Sustainability, MDPI, vol. 7(9), pages 1-22, September.
    91. David, Bounie & Abel, François & Patrick, Waelbroeck, 2016. "Debit card and demand for cash," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 55-66.
    92. Klaus Moeltner & James J. Murphy & John K. Stranlund & Maria Alejandra Velez, 2013. "Institutional heterogeneity in social dilemma games: a Bayesian examination," Chapters, in: John A. List & Michael K. Price (ed.), Handbook on Experimental Economics and the Environment, chapter 2, pages 67-88, Edward Elgar Publishing.
    93. 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.
    94. Milas, Costas & Panagiotidis, Theodore & Papapanagiotou, Georgios, 2024. "UK Foreign Direct Investment in uncertain economic times," Journal of International Money and Finance, Elsevier, vol. 147(C).
    95. Xiaodong Du & Fengxia Dong, 2016. "Responses to market information and the impact on price volatility and trading volume: the case of Class III milk futures," Empirical Economics, Springer, vol. 50(2), pages 661-678, March.
    96. Thaden, Hauke & Klein, Nadja & Kneib, Thomas, 2019. "Multivariate effect priors in bivariate semiparametric recursive Gaussian models," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 51-66.
    97. 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.
    98. Maksym, Obrizan, 2010. "A Bayesian Model of Sample Selection with a Discrete Outcome Variable," MPRA Paper 28577, University Library of Munich, Germany.
    99. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    100. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    101. Nurmakhanova, Mira, 2008. "Essays on fall fertilizer application," ISU General Staff Papers 2008010108000016739, Iowa State University, Department of Economics.
    102. Gary Koop & Dimitris Korobilis, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Working Paper series 47_09, Rimini Centre for Economic Analysis.
    103. Blazejowski, Marcin & Kufel, Paweł & Kwiatkowski, Jacek, 2018. "Model simplification and variable selection: A Replication of the UK inflation model by Hendry (2001)," MPRA Paper 88745, University Library of Munich, Germany.
    104. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    105. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2013. "Historical Developments in Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 13-191/III, Tinbergen Institute.
    106. Odusola, Ayodele & Abidoye, Babatunde, 2015. "Effects of Temperature and Rainfall Shocks on Economic Growth in Africa," UNDP Africa Research Discussion Papers 267028, United Nations Development Programme (UNDP).
    107. Ishdorj, Ariun, 2008. "Essays on food assistance program participation and demand for food," ISU General Staff Papers 2008010108000016750, Iowa State University, Department of Economics.
    108. Park, Yuri & Koo, Yoonmo, 2016. "An empirical analysis of switching cost in the smartphone market in South Korea," Telecommunications Policy, Elsevier, vol. 40(4), pages 307-318.
    109. Coulibaly, Issiaka & Gnimassoun, Blaise, 2013. "Optimality of a monetary union: New evidence from exchange rate misalignments in West Africa," Economic Modelling, Elsevier, vol. 32(C), pages 463-482.
    110. Damien, Paul & Fuentes-García, Ruth & Mena, Ramsés H. & Zarnikau, Jay, 2019. "Impacts of day-ahead versus real-time market prices on wholesale electricity demand in Texas," Energy Economics, Elsevier, vol. 81(C), pages 259-272.
    111. Habtamu Kiros & Alebachew Abebe, 2020. "Statistical Modeling of Women Employment Status at Harari Region Urban Districts: Bayesian Approach," Annals of Data Science, Springer, vol. 7(1), pages 63-76, March.
    112. Brendan Kline & Justin L. Tobias, 2008. "The wages of BMI: Bayesian analysis of a skewed treatment-response model with nonparametric endogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(6), pages 767-793.
    113. Marcin Błażejowski & Jacek Kwiatkowski & Paweł Kufel, 2020. "BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl," Econometrics, MDPI, vol. 8(2), pages 1-29, May.
    114. Qian, Hang, 2010. "Linear regression using both temporally aggregated and temporally disaggregated data: Revisited," MPRA Paper 32686, University Library of Munich, Germany.
    115. Colson, Gregory, 2009. "Improving nutrient content through genetic modification: Evidence from experimental auctions on consumer acceptance and willingness to pay for intragenic foods," ISU General Staff Papers 200901010800001872, Iowa State University, Department of Economics.
    116. Gary J. Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2019. "Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 275-292, Emerald Group Publishing Limited.
    117. Obryan Poyser, 2019. "Exploring the dynamics of Bitcoin’s price: a Bayesian structural time series approach," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 9(1), pages 29-60, March.
    118. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    119. Shinya Sugawara & Yasuhiro Omori, 2012. "An Econometric Analysis of Insurance Markets with Separate Identification for Moral Hazard and Selection Problems," CIRJE F-Series CIRJE-F-849, CIRJE, Faculty of Economics, University of Tokyo.
    120. Jerzy Marzec & Andrzej Pisulewski, 2020. "Pomiar efektywności zróżnicowanych technologicznie gospodarstw rolnych w Unii Europejskiej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 111-137.
    121. Abidoye, Babatunde O & Mabaya, Edward, 2014. "Adoption of genetically modified crops in South Africa: Effects on wholesale maize prices," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 53(1), February.
    122. Ronaldo Carpio & Meixin Guo, 2021. "Bayesian estimation of the Eurozone currency union effect," Review of International Economics, Wiley Blackwell, vol. 29(3), pages 511-532, August.
    123. Shinya Sugawara & Yasuhiro Omori, 2013. "An Econometric Analysis of Insurance Markets with Separate Identification for Moral Hazard and Selection," CIRJE F-Series CIRJE-F-882, CIRJE, Faculty of Economics, University of Tokyo.
    124. Nataliia Ostapenko, 2022. "Do output gap estimates improve inflation forecasts in Slovakia?," Working and Discussion Papers WP 4/2022, Research Department, National Bank of Slovakia.
    125. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
    126. Hakobyana, Zaruhi & Koulovatianos, Christos, 2019. "Populism and polarization in social media without fake news: The vicious circle of biases, beliefs and network homophily," CFS Working Paper Series 626, Center for Financial Studies (CFS).
    127. Christopher Moore & Daniel Phaneuf & Walter Thurman, 2011. "A Bayesian Bioeconometric Model of Invasive Species Control: The Case of the Hemlock Woolly Adelgid," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 50(1), pages 1-26, September.
    128. James R. Bland, 2019. "Measuring and Comparing Two Kinds of Rationalizable Opportunity Cost in Mixture Models," Games, MDPI, vol. 11(1), pages 1-27, December.
    129. Moisés Carrasco Garcés & Felipe Vasquez-Lavin & Roberto D. Ponce Oliva & José Luis Bustamante Oporto & Manuel Barrientos & Arcadio A. Cerda, 2021. "Embedding effect and the consequences of advanced disclosure: evidence from the valuation of cultural goods," Empirical Economics, Springer, vol. 61(2), pages 1039-1062, August.
    130. Blazejowski, Marcin & Kwiatkowski, Jacek, 2020. "Bayesian Model Averaging for Autoregressive Distributed Lag (BMA_ADL) in gretl," MPRA Paper 98387, University Library of Munich, Germany.
    131. Metin Çakır, 2022. "Retail pass‐through of package downsizing," Agribusiness, John Wiley & Sons, Ltd., vol. 38(2), pages 259-278, April.
    132. Koirala, Niraj P. & Nyiwul, Linus, 2023. "Inflation volatility: A Bayesian approach," Research in Economics, Elsevier, vol. 77(1), pages 185-201.
    133. Liu, De-Chih & Chang, Yu-Chien, 2022. "Systematic variations in exchange rate returns," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 569-583.
    134. Babatunde O Abidoye & Edward Mabaya, 2014. "Adoption of genetically modified crops in South Africa: Effects on wholesale maize prices," Agrekon, Taylor & Francis Journals, vol. 53(1), pages 104-123, March.
    135. Yi-Chun (Chad) Ho & Junjie Wu & Yong Tan, 2017. "Disconfirmation Effect on Online Rating Behavior: A Structural Model," Information Systems Research, INFORMS, vol. 28(3), pages 626-642, September.
    136. Kim, Namhoon & Mountain, Travis P., 2018. "Do we consider paid sick leave when deciding to get vaccinated?," Social Science & Medicine, Elsevier, vol. 198(C), pages 1-6.
    137. Xiong, Yingge & Mannering, Fred L., 2013. "The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 39-54.
    138. Nikhil Patel, 2016. "International Trade Finance and the Cost Channel of Monetary Policy in Open Economies," BIS Working Papers 539, Bank for International Settlements.
    139. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2023. "Robust dynamic space–time panel data models using $$\varepsilon $$ ε -contamination: an application to crop yields and climate change," Empirical Economics, Springer, vol. 64(6), pages 2475-2509, June.

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