主    题：Partially Linear Functional-Coefficient Dynamic Panel Data Models: Sieve Estimation and Specification Testing
时    间：10月19日（星期三）下午14：00-15：30
地    点：后主楼1610
主讲人：章永辉，中国人民大学经济学院助理教授
主持人：罗楚亮，北京师范大学教授

【摘要】
In this paper, we study the nonparametric estimation and testing for the partially linear functional-coefficient dynamic panel data models where the effects of some covariates on the dependent variable vary according to a set of low-dimensional conditional variables nonparametrically. Based on the sieve approximation of unknown functions, we propose a sieve 2SLS procedure to estimate the model. The asymptotic properties for both parametric and nonparametric components are established when sample size N and T tend to infinity jointly or only N goes to infinity. We also propose a specification test for the constancy of unknown functions based on a weighted empirical L2-norm distance between the two estimates under the null and the alternative, respectively. We show that after being appropriately standardized, our test is asymptotically normally distributed under the null hypothesis and can detect local alternatives with rate Op(K1/4/√NT). Monte Carlo simulations show that our sieve estimators and test perform remarkably well in finite samples. We apply our method to study the effect of income on democracy and find strong evidence of nonconstancy for the slope coefficient of income.

【Key words】Dynamic panel, Sieve approximation, Functional coefficient, 2SLS estimation