Bayesian analysis of a Tobit quantile regression model

Abstract

This paper develops a Bayesian framework for Tobit quantile regression. Our approach is organized around a likelihood function that is based on the asymmetric Laplace dis- tribution, a choice that turns out to be natural in this context. We discuss families of prior distribution on the quantile regression vector that lead to proper posterior distributions with ¯nite moments. We show how the posterior distribution can be sampled and summarized by Markov chain Monte Carlo methods. A method for com- paring alternative quantile regression models is also developed and illustrated. The techniques are illustrated with both simulated and real data. In particular, in an em- pirical comparison, our approach out-performed two other common classical estimators.