ANALYSIS OF MULTIVARIATE PROBIT MODELS

This paper provides a practical simulation-based Bayesian and non-Baye sian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte C arlo methods and maximum likelihood estimates are obtained by a Monte Carlo version of the EM algorithm. A practical approach for the comput ation of Bayes factors from the simulation output is also developed. T he methods are applied to a dataset with a bivariate binary response, to a four-year longitudinal dataset from the Six Cities study of the h ealth effects of air pollution and to a seven-variate binary response dataset on the labour supply of married women from the Panel Survey of Income Dynamics.