Analysis of Multivariate Probit Models
Washington University in Saint Louis - John M. Olin Business School
Washington University in St. Louis
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 Monte Carlo version of the EM 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.
JEL Classification: C11, C15working papers series
Date posted: October 8, 1996
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