Particle Rolling MCMC with Double-Block Sampling

Naoki Awaya and Yasuhiro Omori
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Naoki Awaya: Graduate School of Economics, The University of Tokyo
Yasuhiro Omori: Faculty of Economics, The University of Tokyo

No CIRJE-F-1175, CIRJE F-Series from CIRJE, Faculty of Economics, 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.

Pages: 43 pages
Date: 2021-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-isf
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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2021cf1175

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