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Bayesian Inference for Periodic Regime-Switching Models

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  • Eric Ghysels

    ()

  • Robert E. McCulloch
  • Ruey S. Tsay

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 nontrivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the competitional burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical examples as illustration, one using housing starts data while the other covers U.S. post WWII individual production. Nous présentons une classe générale de modèles non-linéaires avec changement de régime Markovienne. Les modèles proposés permettent d'avoir une structure périodique pour la chaîne de Markov ainsi que des effets saisonniers dans chaqu'un des régimes. La classe de structure proposée permet d'avoir des interdépendences entre les fluctuationssaisonnières, les cycles d'affaire et la composante de croissance. Une méthode Baysienne basée sur le principe de l'échantillonage de Gibbs est utilisée pour estimation et interférence. Deux exemples empiriques sont fournis, un premier utilisant des séries de mise en chantier de0501sons, tandis que le second couvre la production industrielle aux États-Unis.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 94s-15.

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Date of creation: 01 Jan 1994
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Handle: RePEc:cir:cirwor:94s-15

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Keywords: Markov switching; Periodic models; Seasonality; Gibbs sampler; Modèles à changement de régime ; Structure périodique ; Saisonnalité ; Échantillonage de Gibbs;

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References

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  1. Ghysels, Eric, 1994. "On the Periodic Structure of the Business Cycle," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 289-98, July.
  2. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
  3. Ghysels, E., 1993. "A Time Series Model with Periodic Stochastic Regime Switching," Cahiers de recherche 9314, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  4. 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 307-28, April.
  5. 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 357-84, March.
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Cited by:
  1. Smith, Aaron D. & Naik, Prasad A. & Tsai, Chih-Ling, 2005. "Markov-Switching Model Selection Using Kullback-Leibler Divergence," Working Papers 11976, University of California, Davis, Department of Agricultural and Resource Economics.
  2. 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.
  3. 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.
  4. 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.

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