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Econometrics 2016, 4(1), 12; doi:10.3390/econometrics4010012

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Department of Economics, Laval University, 2216 Pavillon J.-A.-DeSève, QC G1V 0A6, Canada
Academic Editors: Roberto Casarin, Francesco Ravazzolo, Herman K. van Dijk and Nalan Basturk
Received: 24 August 2015 / Revised: 27 December 2015 / Accepted: 28 January 2016 / Published: 8 March 2016
(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)
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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. View Full-Text
Keywords: bayesian inference; sequential monte carlo; annealed importance sampling; change-point models; differential evolution; GARCH models bayesian inference; sequential monte carlo; annealed importance sampling; change-point models; differential evolution; GARCH models

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dufays, A. Evolutionary Sequential Monte Carlo Samplers for Change-Point Models. Econometrics 2016, 4, 12.

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