Estimating Marginal Likelihoods for Mixture and Markov Switching Models Using Bridge Sampling Techniques

25 Pages Posted: 9 Jul 2004

See all articles by Sylvia Fruhwirth-Schnatter

Sylvia Fruhwirth-Schnatter

Johannes Kepler University - Department of Applied Statistics and Econometrics

Abstract

This paper discusses the problem of estimating marginal likelihoods for mixture and Markov switching model. Estimation is based on the method of bridge sampling (Meng and Wong 1996; Statistica Sinica 11, 552-86.) where Markov Chain Monte Carlo (MCMC) draws from the posterior density are combined with an i.i.d. sample from an importance density. The importance density is constructed in an unsupervised manner from the MCMC draws using a mixture of complete data posteriors. Whereas the importance sampling estimator as well as the reciprocal importance sampling estimator are sensitive to the tail behaviour of the importance density, we demonstrate that the bridge sampling estimator is far more robust. Our case studies range from computing marginal likelihoods for a mixture of multivariate normal distributions, testing for the inhomogeneity of a discrete time Poisson process, to testing for the presence of Markov switching and order selection in the MSAR model.

Keywords: Bayesian model choice, bridge sampling technique, marginal likelihoods, Markov switching models, mixture models

Suggested Citation

Fruhwirth-Schnatter, Sylvia, Estimating Marginal Likelihoods for Mixture and Markov Switching Models Using Bridge Sampling Techniques. Econometrics Journal, Vol. 7, No. 1, pp. 143-167, June 2004. Available at SSRN: https://ssrn.com/abstract=558835

Sylvia Fruhwirth-Schnatter (Contact Author)

Johannes Kepler University - Department of Applied Statistics and Econometrics ( email )

Altenbergerstrasse 69
Linz
Austria

Register to save articles to
your library

Register

Paper statistics

Downloads
9
Abstract Views
928
PlumX Metrics