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An effcient exact Bayesian method For state space models with stochastic volatility

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  • Huang Yu-Fan

    (Capital University of Economics and Business, International School of Economics and Management, 121 Zhangjialukou, Huaxiang Fengtai District, Beijing, China)

Abstract

This paper introduces a Bayesian MCMC method, referred to as a marginalized mixture sampler, for state space models whose disturbances follow stochastic volatility processes. The marginalized mixture sampler is based on a mixture-normal approximation of the log-χ 2 distribution, but it is implemented without the need to simulate the mixture indicator variable. The key innovation is to use the filter ing scheme developed by Kim (Kim C.-J. 1994. “Dynamic Linear Models with Markov-Switching.” Journal of Econometrics 60: 1–22.) and the forward-filtering backward-sampling algorithm to generate a proposal series of the latent stochastic volatility process. The proposal series is then accepted according to the Metropolis-Hastings acceptance probability. The new sampler is examined within an unobserved component model and a time-varying parameter vector autoregressive model, and it reduces substantially the correlations between MCMC draws.

Suggested Citation

  • Huang Yu-Fan, 2021. "An effcient exact Bayesian method For state space models with stochastic volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-10, April.
    • Handle: RePEc:bpj:sndecm:v:25:y:2021:i:2:p:10:n:6
    DOI: 10.1515/snde-2018-0098
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    References listed on IDEAS

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