A Bayesian approach for estimation of weight matrices in spatial autoregressive models

We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Our approach focuses on spatial weights which are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori assumptions on (socio-)economic distances between the spatial units. The estimation approach relies on efficient Gibbs sampling techniques and can be easily combined with and extended to more flexible spatial specifications. In addition to geographic prior structures, we also discuss shrinkage priors on the neighbourhood size, which are particularly useful in spatial panels where T is small relative to N.