Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas

We consider the Bayes estimation of a multivariate sample selection model with p pairs of selection and outcome variables. Each of the variables may be discrete or continuous with a parametric marginal distribution, and their dependence structure is modeled through a Gaussian copula function. Markov chain Monte Carlo methods are used to simulate from the posterior distribution of interest. The methods are demonstrated on simulated data and an application from transportation economics.