A Bayesian approach to dynamic Tobit models

This paper develops a posterior simulation method for a dynamic Tobit model. The major ob- stacle rooted in such a problem lies in high dimensional integrals, induced by dependence among censored observations, in the likelihood function. The primary contribution of this research is to develop a practical and efficient sampling scheme for the conditional posterior distributions of the censored (i.e.unobserved) data, so that the Gibbs sampler with data augmentation al- gorithm is successfully applied. The substantial differences between this approach and some existing methods are highlighted. The proposed simulation method is investigated by means of a Monte Carlo study and applied to a regression model of Japanese exports of passenger cars to the U.S. subject to a non-tariff trade barrier.