Bayesian estimation of limited dependent variable spatial autoregressive models

A Gibbs sampling (Markov chain Monte Carbo) method for estimating spatial autoregressive limited dependent variable models is presented. The method can accommodate data sets containing spatial outliers and general forms of nonconstant variance. It is argued that there are several advantages to the method proposed here relative to that proposed and illustrated in McMillen (1992) for spatial probit models.