This paper investigates estimation of semiparametric varying-coefficient spatial autoregressive models in which the dependent variable is missing at random. An inverse propensity score weighted sieve two-stage least squares (S-2SLS) estimation with imputation is proposed. The proposed estimators are shown to be consistent, no matter the initial value is taken as the naive S-2SLS estimate or the naive nonlinear least squares estimate, and the asymptotic distribution of the latter is also derived. Simulation studies are carried out to investigate the performance of the proposed estimator. The method is finally exemplified with one real data set on Boston housing prices.

本文研究了因变量随机缺失的半参数变系数空间自回归模型的估计。提出了一种带插值的倾向得分加权筛分两阶段最小二乘（S-2SLS）估计方法。无论初始值是朴素的S-2SLS估算还是朴素的非线性最小二乘估算，所提出的估计量都是一致的，并且还推导了后者的渐近分布。进行了仿真研究，以研究所提出的估计器的性能。该方法最后以一组有关波士顿住房价格的真实数据为例。