Estimation and inference in functional-coefficient spatial autoregressive panel data models with fixed effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors with a large number of cross-sectional units and a finite time periods. Our proposed methodology removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, however avoids using backfitting technique. We derive the limiting results for the proposed estimators and construct a consistent nonparametric test to test for spatial endogeneity. A small Monte Carlo study shows that our proposed estimators and test statistic exhibit good finite-sample performance.