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Randomized Quasi Sequential Markov Chain Monte Carlo²

Author

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  • Fabian Goessling

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.

Suggested Citation

  • Fabian Goessling, 2018. "Randomized Quasi Sequential Markov Chain Monte Carlo²," CQE Working Papers 7018, Center for Quantitative Economics (CQE), University of Muenster.
    • Handle: RePEc:cqe:wpaper:7018
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    File URL: https://www.wiwi.uni-muenster.de/cqe/sites/cqe/files/CQE_Paper/cqe_wp_70_2018.pdf
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    References listed on IDEAS

    as
    1. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    2. Jin-Chuan Duan & Andras Fulop, 2015. "Density-Tempered Marginalized Sequential Monte Carlo Samplers," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 192-202, April.
    3. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    4. Mathieu Gerber & Nicolas Chopin, 2015. "Sequential quasi Monte Carlo," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(3), pages 509-579, June.
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    Cited by:

    1. Fabian Goessling, 2018. "Human Capital, Growth, and Asset Prices," CQE Working Papers 6918, Center for Quantitative Economics (CQE), University of Muenster.

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    More about this item

    Keywords

    SMC; MCMC; Bayesian Estimation; Filtering;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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