GMM estimation of spatial autoregressive models in a system of simultaneous equations with heteroskedasticity
针对异方差扰动下的联立方程组空间自回归模型，提出一种利用线性和二次矩条件的GMM估计方法，比QML更易实现且稳健，蒙特卡洛实验显示有限样本表现良好。
This paper proposes a GMM estimation framework for the SAR model in a system of simultaneous equations with heteroskedastic disturbances. Besides linear moment conditions, the proposed GMM estimator also utilizes quadratic moment conditions based on the covariance structure of model disturbances within and across equations. Compared with the QML approach, the GMM estimator is easier to implement and robust under heteroskedasticity of unknown form. We derive the heteroskedasticity-robust standard error for the GMM estimator. Monte Carlo experiments show that the proposed GMM estimator performs well in finite samples.