  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.
