Profile GMM Estimation of Panel Data Models with Interactive Fixed Effects

（交互固定效应面板数据模型的广义矩估计）

主讲人：洪圣杰（中央财经大学）

主持老师：（北大经院）王熙

参与老师：（北大经院）王一鸣、刘蕴霆

（北大国发院）沈艳、黄卓、张俊妮、孙振庭

（北大新结构经济学研究院）胡博

时间：2021年12月3日（周五） 10:00 -- 11:30

地点（线下）： 北大经济学院107

主讲人简介：

洪圣杰，2012年于威斯康辛大学麦迪逊分校取得经济学博士学位，现任职于中央财经大学经济学院。主要研究领域为：计量经济学理论、应用微观和中国经济。研究成果发表在Journal of Econometrics, Journal of Comparative Economics,《管理世界》和《金融研究》等国际和国内知名期刊。

摘要：

This paper studies panel data models with interactive fixed effects where the regressors are allowed to be correlated with the idiosyncratic error terms. We propose a two-step profile GMM estimation procedure to estimate the parameters of interest. In the first step we obtain a preliminary consistent estimate of the slope coefficient via a nuclear-norm-regularization (NNR) based profile GMM procedure. In the second step, via an iterative procedure, we conduct post-NNR profile GMM estimation of the slope coefficient, factors, and factor loadings, with an improved convergence rate for the estimate of the slope coefficient. We establish the asymptotic properties of the preliminary estimates and the iterative estimates, and propose an efficient profile GMM estimator. We also study the determination of the number of factors and propose Hausman tests for the exogeneity of the regressor. Monte Carlo simulations suggest that the proposed estimation and testing methods work well in the determination of the number of factors, the estimation of the model parameters and the test for exogeneity. As an empirical application, we apply our model and method to study the price elasticity of U.S. imports.