Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models
研究空间自回归模型中极大似然和拟极大似然估计量的渐近性质，发现估计量的收敛速度取决于空间权重矩阵特征，当每个单元只受少数邻居影响时估计量有√n收敛速度和渐近正态性，否则信息矩阵可能不规则且各分量收敛速度不同。
This paper investigates asymptotic properties of the maximum likelihood estimator and the quasi-maximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model. It is important to make the distinction with different spatial scenarios. Under the scenario that each unit will be influenced by only a few neighboring units, the estimators may have √n-rate of convergence and be asymptotically normal. When each unit can be influenced by many neighbors, irregularity of the information matrix may occur and various components of the estimators may have different rates of convergence.