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The Gibbs sampler with particle efficient importance sampling for state-space models

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  • Oliver Grothe
  • Tore Selland Kleppe
  • Roman Liesenfeld

Abstract

We consider Particle Gibbs (PG) for Bayesian analysis of non-linear non-Gaussian state-space models. As a Monte Carlo (MC) approximation of the Gibbs procedure, PG uses sequential MC (SMC) importance sampling inside the Gibbs to update the latent states. We propose to combine PG with the Particle Efficient Importance Sampling (PEIS). By using SMC sampling densities which are approximately globally fully adapted to the targeted density of the states, PEIS can substantially improve the simulation efficiency of the PG relative to existing PG implementations. The efficiency gains are illustrated in PG applications to a non-linear local-level model and stochastic volatility models.

Suggested Citation

  • Oliver Grothe & Tore Selland Kleppe & Roman Liesenfeld, 2019. "The Gibbs sampler with particle efficient importance sampling for state-space models," Econometric Reviews, Taylor & Francis Journals, vol. 38(10), pages 1152-1175, November.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:10:p:1152-1175
    DOI: 10.1080/07474938.2018.1536098
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    Cited by:

    1. Mengheng Li & Marcel Scharth, 2022. "Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochastic Volatility Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 285-301, January.
    2. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.

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