Evolutionary Sequential Monte Carlo Samplers for Change-point Models

Vous êtes ici

Accueil » Evolutionary Sequential Monte Carlo Samplers for Change-point Models
02 Septembre 2015
Types de publication: 
Cahier de recherche
Arnaud Dufays
Axe de recherche: 
Enjeux économiques et financiers
Bayesian inference
Sequential Monte Carlo
Annealed Importance sampling
Change-point models
Differential Evolution
GARCH models
Classification JEL: 

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.


Dufays : Département d’économique and CIRPÉE, arnaud.dufays@ecn.ulaval.ca