Best linear and quadratic moments for spatial econometric models with an application to spatial interdependence patterns of employment growth in US counties
提出一种构建最佳线性与二次矩的分析方法，用于广义矩估计，比拟极大似然估计更有效，并应用于美国县域就业增长数据，揭示空间相互依赖模式。
Summary We provide a novel analytic procedure to construct best linear and quadratic moments of the generalized method of moments estimation for a large class of cross‐sectional network and spatial econometric models. These moments generate an estimator that is asymptotically more efficient than the quasi‐maximum likelihood estimator when the disturbances follow a non‐normal and unknown distribution. We apply this procedure to a high‐order spatial autoregressive model with spatial errors, where the disturbances are heteroskedastic. Two normality tests of disturbances are developed. We apply the model to employment data in US counties, which demonstrates spatial interdependence patterns of regional employment growth.