Data Augmentation in the Bayesian Multivariate Probit Model

Abstract

This paper is concerned with the Bayesian estimation of a Multivariate Probit model. In particular, this paper provides an algorithm that obtains draws with low correlation much faster than a pure Gibbs sampling algorithm. The algorithm consists in sampling some characteristics of slope and variance parameters marginally on the latent data. Estimations with simulated datasets illustrate that the proposed algorithm can be much faster than a pure Gibbs sampling algorithm. For some datasets, the algorithm is also much faster than the efficient algorithm proposed by Liu and Wu (1999) in the context of the univariate Probit model