Evolutionary Sequential Monte Carlo Samplers for Change-point Models

Arnaud Dufays

Cahiers de recherche from Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques

Abstract: Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. Nevertheless the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only SMC algorithms draw posterior distributions of static or dynamic parameters but additionally provide an estimate of the marginal likelihood. The tempered and time (TNT) algorithm, developed in the paper, combines (off-line) tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are already available. The algorithm is notably appropriate for estimating Change-point models. As an example, we compare Change-point GARCH models through their marginal likelihoods over time.

Keywords: Bayesian inference; Sequential Monte Carlo; Annealed Importance sampling; Change-point models; Differential Evolution; GARCH models (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C58 (search for similar items in EconPapers)
Date: 2015
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Related works:
Journal Article: Evolutionary Sequential Monte Carlo Samplers for Change-Point Models (2016)
Working Paper: Evolutionary Sequential Monte Carlo Samplers for Change-point Models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:lvl:crrecr:1508

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