Fitting Spatial Econometric Models through the Unilateral Approximation.

Maximum likelihood estimation of spatial models based on weight matrices typically requires a sizeable computational capacity, even in rel- atively small samples. The unilateral approximation approach to spatial models estimation has been suggested in Besag (1974) as a viable alternat- ive to MLE for conditionally specified processes. In this paper we revisit the method, extend it to simultaneous spatial processes and study the finite-sample properties of the resulting estimators by means of Monte Carlo simulations, using several Conditional Autoregressive Models. Ac- cording to the results, the performance of the unilateral estimators is very good, both in terms of statistical properties (accuracy and precision) and in terms of computing time.