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Bayesian inference for periodic regime-switching models

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  • Eric Ghysels

    (Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA 16802-3306, USA and Centre interuniversitaire de recherche en analyse des organizations (CIRANO), Montréal, Québec H3A 2A5, Canada)

  • Robert E. McCulloch

    (Graduate School of Business, University of Chicago, Chicago, IL. 60637, USA)

  • Ruey S. Tsay

    (Graduate School of Business, University of Chicago, Chicago, IL. 60637, USA)

Abstract

We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical examples as illustration, one uses housing starts data while the other employs US post-Second World War industrial production. © 1998 John Wiley & Sons, Ltd.

Suggested Citation

  • Eric Ghysels & Robert E. McCulloch & Ruey S. Tsay, 1998. "Bayesian inference for periodic regime-switching models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(2), pages 129-143.
    • Handle: RePEc:jae:japmet:v:13:y:1998:i:2:p:129-143
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    References listed on IDEAS

    as
    1. Eric Ghysels, 1993. "A time series model with periodic stochastic regime switching," Discussion Paper / Institute for Empirical Macroeconomics 84, Federal Reserve Bank of Minneapolis.
    2. Ghysels, Eric, 1994. "On the Periodic Structure of the Business Cycle," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 289-298, July.
    3. Robert E. McCulloch & Ruey S. Tsay, 1994. "Statistical Analysis Of Economic Time Series Via Markov Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 523-539, September.
    4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    5. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    6. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-328, April.
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    Cited by:

    1. Bailliu, Jeannine & Dib, Ali & Kano, Takashi & Schembri, Lawrence, 2014. "Multilateral adjustment, regime switching and real exchange rate dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 27(C), pages 68-87.
    2. Smith, Aaron & Naik, Prasad A. & Tsai, Chih-Ling, 2006. "Markov-switching model selection using Kullback-Leibler divergence," Journal of Econometrics, Elsevier, vol. 134(2), pages 553-577, October.
    3. Christiano, Lawrence J. & Todd, Richard M., 2002. "The conventional treatment of seasonality in business cycle analysis: does it create distortions?," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 335-364, March.
    4. Jaehee Kim & Sooyoung Cheon, 2010. "A Bayesian regime‐switching time‐series model," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 365-378, September.
    5. Zhao-Hua Lu & Sy-Miin Chow & Nilam Ram & Pamela M. Cole, 2019. "Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 611-645, June.
    6. Carvalho, Alexandre X. & Tanner, Martin A., 2007. "Modelling nonlinear count time series with local mixtures of Poisson autoregressions," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5266-5294, July.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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