GMM estimation of spatial autoregressive models with unknown heteroskedasticity

Abstract

In the presence of heteroskedastic disturbances, the MLE for the SAR models without taking into account the heteroskedasticity is generally inconsistent. The 2SLS estimates can have large variances and biases for cases where regressors do not have strong effects. In contrast, GMM estimators obtained from certain moment conditions can be robust. Asymptotically valid inferences can be drawn with consistently estimated covariance matrices. Efficiency can be improved by constructing the optimal weighted estimation. The approaches are applied to the study of county teenage pregnancy rates. The empirical results show a strong spatial convergence among county teenage pregnancy rates.