讲座题目：Two-Step Estimation of Quantile Panel Data Models with Interactive Fixed Effects
报告地点：线下 经管院A221 线上 腾讯会议278 167 890
内容摘要：This paper considers the estimation of panel data models with interactive fixed effects where the idiosyncratic errors are subject to conditional quantile restrictions. I propose a two-step estimator for the coefficient of the observed regressors that is easy to implement in practice. In the first step, the principal component analysis is applied to the cross-sectional averages of the regressors to estimate the latent factors. In the second step, the smoothed quantile regression is used to estimate the coefficient of the observed regressors and the factor loadings jointly. The consistency and asymptotic normality of the estimator are established under large N, T asymptotics. It is found that the asymptotic distribution of the estimator suffers from asymptotic biases, and I show how to correct the biases using both analytical and split-panel jackknife bias corrections. Simulation studies confirm that the proposed es- timator performs well with moderate sample sizes.
主讲人简介：Dr.Liang Chen is an Assistant Professor in HSBC Business School, Peking University. His research interets lies in the domain of theoretical econometrics, focusing on quantile, panel data and factor models. After Graduating from the Universidad Carlos III de Madrid, he was a postdoc researcher in Oxford University. Before joining the HSBC Business School, he was an Assistant Professor in Shanghai University of Finance and Economics. He has published several papers in leading international journals, including Journal of Econometrics, Econometric Theory and Economic Letters.