Randomized Quasi Sequential Markov Chain Monte Carlo²

Fabian Goessling

No 7018, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

Abstract: Sequential Monte Carlo and Markov Chain Monte Carlo methods are combined into a unifying framework for Bayesian parameter inference in non-linear, non-Gaussian state space models. A variety of tuning approaches are suggested to boost convergence: likelihood tempering, data tempering, adaptive proposals, random blocking, and randomized Quasi Monte Carlo numbers. The methods are illustrated and compared by running eight variants of the algorithm to estimate the parameters of a standard stochastic volatility model.

Keywords: SMC; MCMC; Bayesian Estimation; Filtering (search for similar items in EconPapers)
JEL-codes: C11 C13 C32 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2018-02
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:cqe:wpaper:7018

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