Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Arnaud Dufays

Econometrics, 2016, vol. 4, issue 1, 1-33

Abstract: Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, 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 do SMC algorithms draw posterior distributions of static or dynamic parameters but additionally they provide an estimate of the marginal likelihood. The tempered and time (TNT) algorithm, developed in this 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 readily available. The algorithm is notably appropriate for estimating change-point models. As an example, we compare several change-point GARCH models through their marginal log-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: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Evolutionary Sequential Monte Carlo Samplers for Change-point Models (2015)
Working Paper: Evolutionary Sequential Monte Carlo Samplers for Change-point Models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:4:y:2016:i:1:p:12-:d:65253

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