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Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework

Author

Listed:
  • Hwu Shih-Tang

    (Department of Economics, California State Polytechnic University, Pomona, USA)

  • Kim Chang-Jin

    (Department of Economics, University of Washington, Seattle, USA)

Abstract

The basic Markov-switching model has been extended in various ways ever since the seminal work of Hamilton (1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57: 357–84). However, the estimation of Markov-switching models in the literature has relied upon parametric assumptions on the distribution of the error term. In this paper, we present a Bayesian approach for estimating Markov-switching models with unknown and potentially non-normal error distributions. We approximate the unknown distribution of the error term by the Dirichlet process mixture of normals, in which the number of mixtures is treated as a parameter to estimate. In doing so, we pay special attention to the identification of the model. We then apply the proposed model and MCMC procedure to the growth of the postwar U.S. industrial production index. Our model can effectively control for irregular components that are not related to business conditions. This leads to sharp and accurate inferences on recession probabilities.

Suggested Citation

  • Hwu Shih-Tang & Kim Chang-Jin, 2024. "Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 177-199, April.
    • Handle: RePEc:bpj:sndecm:v:28:y:2024:i:2:p:177-199:n:1
    DOI: 10.1515/snde-2022-0055
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    References listed on IDEAS

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    1. Luc Bauwens & Jean-François Carpantier & Arnaud Dufays, 2017. "Autoregressive Moving Average Infinite Hidden Markov-Switching Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 162-182, April.
    2. Jan Bulla & Sascha Mergner & Ingo Bulla & André Sesboüé & Christophe Chesneau, 2011. "Markov-switching asset allocation: Do profitable strategies exist?," Journal of Asset Management, Palgrave Macmillan, vol. 12(5), pages 310-321, November.
    3. Yunjong Eo & Chang-Jin Kim, 2016. "Markov-Switching Models with Evolving Regime-Specific Parameters: Are Postwar Booms or Recessions All Alike?," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 940-949, December.
    4. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    5. Hamilton, J.D., 2016. "Macroeconomic Regimes and Regime Shifts," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 163-201, Elsevier.
    6. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
    7. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    8. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
    9. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
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    More about this item

    Keywords

    label switching problem; identification condition; unknown error distribution; mixture of normals; semi-parametric Bayesian inference; Markov chain Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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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