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Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

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Volume 24 (2020)

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

Yu-Fan Huang
  • Corresponding author
  • Capital University of Economics and Business, International School of Economics and Management, 121 Zhangjialukou, Huaxiang Fengtai District, Beijing, China
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Published Online: 2020-02-14 | DOI: https://doi.org/10.1515/snde-2018-0098


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.

This article offers supplementary material which is provided at the end of the article.

Keywords: Bayesian MCMC; state space; stochastic volatility


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About the article

Published Online: 2020-02-14

Funding Source: National Natural Science Foundation of China

Award identifier / Grant number: 71601129

National Natural Science Foundation of China, Funder Id: http://dx.doi.org/10.13039/501100001809, Grant Number: 71601129.

Citation Information: Studies in Nonlinear Dynamics & Econometrics, 20180098, ISSN (Online) 1558-3708, DOI: https://doi.org/10.1515/snde-2018-0098.

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