QML and Efficient GMM Estimation of Spatial Autoregressive Models with Dominant (Popular) Units
研究了空间权重矩阵列和可能非一致有界时，空间自回归模型的QML和GMM估计，推导了渐近分布，蒙特卡洛实验显示有限样本表现良好，但列和阶数为O(n)时表现不佳，并应用于贸易网络主导单元的增长收敛分析。
This article investigates QML and GMM estimation of spatial autoregressive (SAR) models in which the column sums of the spatial weights matrix might not be uniformly bounded. We develop a central limit theorem in which the number of columns with unbounded sums can be finite or infinite and the magnitude of their column sums can be O(nδ) if δ<1. Asymptotic distributions of QML and GMM estimators are derived under this setting, including the GMM estimators with the best linear and quadratic moments when the disturbances are not normally distributed. The Monte Carlo experiments show that these QML and GMM estimators have satisfactory finite sample performances, while cases with a column sums magnitude of <i>O</i>(<i>n</i>) might not have satisfactory performance. An empirical application with growth convergence in which the trade flow network has the feature of dominant units is provided. Supplementary materials for this article are available online.