An effcient exact Bayesian method For state space models with stochastic volatility
Huang Yu-Fan ()
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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
Studies in Nonlinear Dynamics & Econometrics, 2021, vol. 25, issue 2, 10
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-χ2 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.
Keywords: Bayesian MCMC; state space; stochastic volatility (search for similar items in EconPapers)
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