Often, authors report materially different OLS and spatial error model estimates. However, under the null of correct specification, these estimates should be similar. We propose a spatial Hausman test and conduct a Monte Carlo experiment to examine its performance.

Given local spatial error dependence, one can construct sparse spatial weight matrices. As an illustration of the power of such sparse structures, we computed a simultaneous autoregression using 20 640 observations in under 19 min despite needing to compute a 20 640 by 20 640 determinant 10 times.