Bayesian Analysis of Multivariate Probit Models

Siddhartha Chib and Edward Greenberg
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Siddhartha Chib: Washington University
Edward Greenberg: Washington University

Econometrics from EconWPA

Abstract: This paper provides a unified simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Markov chain Monte Carlo version of the E-M algorithm. Computation of Bayes factors from the simulation output is also considered. The methods are applied to a bivariate data set, to a 534-subject, four-year longitudinal data set from the Six Cities study of the health effects of air pollution, and to a seven-year data set on the labor supply of married women from the Panel Survey of Income Dynamics.

Keywords: Bayes factors; correlated binary data; Gibbs sampling; marginal likelihood; Markov chain Monte Carlo; Metropolis-Hastings algorithm. (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 1996-08-26
Note: Type of Document - ; to print on PostScript; pages: 24
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:9608002

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