Semiparametric spatial autoregressive models with nonlinear endogeneity
构建了非参数两阶段最小二乘和广义矩估计的筛子估计量，用于估计带有内生环境变量的函数系数空间自回归模型，并推导了估计量的一致性和渐近正态性。
.This article constructs nonparametric two-step least squares (2SLS) and generalized method of moments (GMM) sieve estimators to estimate a functional-coefficient spatial autoregressive model with an endogenous environment variable. We derive the consistency and asymptotic normality results for our proposed sieve estimators. A small Monte Carlo study shows that our proposed estimators exhibit good finite-sample performance. An empirical application is used to illustrate the usefulness of our methods.