CIRJE-F-1110 "Particle rolling MCMC"
Author Name

Awaya, Naoki and Yasuhiro Omori

Date January 2019
Full Paper PDF File
Remarks

 Revised version of CIRJE-F-1066 (2017) and CIRJE-F-1080 (2018).

Abstract

An efficient simulation-based methodology is proposed for the rolling window esti- mation of state space models. Using the framework of the conditional sequential Monte Carlo update in the particle Markov chain Monte Carlo estimation, weighted particles are updated to learn and forget the information of new and old observations by the forward and backward block sampling with the particle simulation smoother. These particles are also propagated by the MCMC update step. Theoretical justi cations are provided for the proposed estimation methodology. The computational performance is evaluated in illustrative examples, showing that the posterior distributions of model parameters and marginal likelihoods are estimated with accuracy. Finally, as a special case, our proposed method can be used as a new sequential MCMC based on Particle Gibbs, which is the promising alternative to SMC2 based on Particle MH.