Semiparametric spatial autoregressive models with nonlinear endogeneity

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.