Bayesian Estimation of Spatial Autoregressive Models

Abstract

Spatial econometrics has relied extensively on spatial autoregressive models. Anselin (1988) developed a taxonomy of these models using a regression model framework and maximum likelihood estimation methods. A Bayesian approach to estimating these models based on Gibbs sampling is introduced here. It allows for non-constant variance over space taking an unspecified form and outliers in the sample data. In addition, estimates of the non-constant variance at each point in space allow inferences regarding the spatial nature of heteroskedasticity and the position of outliers.