报告时间：2023年6月26日(周一)上午10:00主楼317（总第380期）

题目:Estimation of spatial autoregressive panel data models with nonparametric endogenous effect

报告人: 虞吉海

摘要：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.

报告人简介：虞吉海现任北京大学光华管理学院商务统计与经济计量系的教授，他的主要研究方向为空间计量经济学和微观数据分析，具体包括对各种静态空间面板模型和动态空间面板模型进行设定、估计及相关的统计推断，同时运用计量工具对区域经济增长、地方政府竞争和大气污染等领域进行实证分析，曾获批国家杰出青年科学基金项目。