Information Theory Estimators for the First-Order Spatial Autoregressive Model

Information theoretic estimators for the first-order spatial autoregressive model are introduced, small sample properties are investigated, and the estimator is applied empirically. Monte Carlo experiments are used to compare finite sample performance of more traditional spatial estimators to three different information theoretic estimators, including maximum empirical likelihood, maximum empirical exponential likelihood, and maximum log Euclidean likelihood. Information theoretic estimators are found to be robust to selected specifications of spatial autocorrelation and may dominate traditional estimators in the finite sample situations analyzed, except for the quasi-maximum likelihood estimator which competes reasonably well. The information theoretic estimators are illustrated via an application to hedonic housing pricing.