Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas

Abstract

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 illustrated in a simulation study and an application from transportation economics.

Citation

Li, P. and Arshad Rahman, M. (2011), "Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas", Drukker, D.M. (Ed.) Missing Data Methods: Cross-sectional Methods and Applications (Advances in Econometrics, Vol. 27 Part 1), Emerald Group Publishing Limited, Leeds, pp. 269-288. https://doi.org/10.1108/S0731-9053(2011)000027A013