Time: 2023/06/19(Mon.), 13:30 – 15:00(Beijing Time)
Title: Estimation of spatial autoregressive panel data models with nonparametric endogenous effect
Venue: Zhonghui Buidling 106
About the speaker:
This paper proposes a sieve generalized method of moments (GMM) method for the estimation of spatial autoregressive panel data models with nonparametric endogenous effect. The new estimator incorporates both linear moments based on the orthogonality of the exogenous regressors with the model disturbances and quadratic moments based on the properties of idiosyncratic errors. We establish the consistency and asymptotic normality of the sieve GMM estimator and show that it is more efficient than the sieve instrumental variable estimator due to additional quadratic moments. We also put forward two new test statistics for testing the linearity of the endogenous effect. Both test statistics are shown to be asymptotic normal under the null and a sequence of local alternatives after proper standardization. Monte Carlo simulations show that the proposed estimators and tests perform well in finite samples. We also apply our method to estimate environmental Kuznets curve in China and knowledge spillover among 30 countries.