GEL estimation and tests of spatial autoregressive models

This paper considers the generalized empirical likelihood (GEL) estimation and tests of spatial autoregressive (SAR) models by exploring an inherent martingale structure. The GEL estimator has the same asymptotic dis-tribution as the generalized method of moments estimator explored with same moment conditions for estimation, but circumvents a first step estimation of the optimal weighting matrix with a preliminary estimator, and thus can be robust to unknown heteroskedasticity and non-normality. While a general GEL removes the asymptotic bias from the preliminary estimator and partially removes the bias due to the correlation between the moment conditions and their Jacobian, the empirical likelihood as a special member of GELs further partially removes the bias from estimating the second moment matrix. We also formulate the GEL overidentfication test, Moran's I test, and GEL ratio tests for parameter restrictions and non-nested hypotheses. While some of the conventional tests might not be robust to non-normality and/or unknown heteroskedasticity, the corresponding GEL tests can.