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Particle Rolling MCMC with Double-Block Sampling

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  • Naoki Awaya

    (Graduate School of Economics, The University of Tokyo)

  • Yasuhiro Omori

    (Faculty of Economics, The University of Tokyo)

Abstract

An efficient particle Markov chain Monte Carlo methodology is proposed for the rollingwindow estimation of state space models. The particles are updated to approximate the long sequence of posterior distributions as we move the estimation window. To overcome the wellknown weight degeneracy problem that causes the poor approximation, we introduce a practical double-block sampler with the conditional sequential Monte Carlo update where we choose one lineage from multiple candidates for the set of current state variables. Our proposed sampler is justified in the augmented space through theoretical discussions. In the illustrative examples, it is shown to be successful to accurately estimate the posterior distributions of the model parameters.

Suggested Citation

  • Naoki Awaya & Yasuhiro Omori, 2021. "Particle Rolling MCMC with Double-Block Sampling ," CIRJE F-Series CIRJE-F-1175, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2021cf1175
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    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2021/2021cf1175.pdf
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    References listed on IDEAS

    as
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    6. 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.
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