Non-Markovian Regime-Switching Models

37 Pages Posted: 27 Apr 2018

See all articles by Chang-Jin Kim

Chang-Jin Kim

Dept. of Economics, University of Washington

Jaeho Kim

Hanyang University - ERICA

Date Written: March 2018


This paper revisits the non-Markovian regime switching model considered by Chib and Dueker (2004), who employ an autoregressive continuous latent variable in order to specify the dynamics of the latent regime-indicator variable. We show that, in spite of the non-Markovian nature of the regime indicator variable, the Markovian property of this continuous latent variable allows us to easily estimate the model within the Bayesian framework without any approximations. In particular, we show that the conventional Gibbs sampling is enough in generating the regime indicator variable as well as the continuous latent variable conditional on all the parameters of the model and data. For an application to business cycle modeling of postwar US real GDP, a modified version of Hamilton’s (1989) Markovian switching model is slightly preferred to a non-Markovian switching model by the Bayesian model selection criterion. For an application to volatility modeling of the weekly stock return, a non-Markovian switching model with endogenous switching or the leverage effect is strongly preferred to Markovian switching models.

Keywords: Non-Markovian Regime Switching, Markovian Regime Switching, Exogenous Switching, Endogenous Switching

JEL Classification: C11, C13, C22, C25

Suggested Citation

Kim, Chang-Jin and Kim, Jaeho, Non-Markovian Regime-Switching Models (March 2018). Available at SSRN: or

Chang-Jin Kim

Dept. of Economics, University of Washington ( email )

Department of Economics (Box 353330)
University of Washington
Seattle, WA 98195-3330
United States

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Jaeho Kim (Contact Author)

Hanyang University - ERICA ( email )

Korea, Republic of (South Korea)

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