Half-Panel Jackknife Fixed Effects Estimation of Panels with Weakly Exogenous Regressor
2017/01/17
研討會日期 : 2017-01-17
時間 : 14:30
主講人 : Dr. Jui-Chung Yang
主持人 : Professor Chu-An Liu
地點 : Conference Room B110
演講者簡介 : Dr. Yang has received his Ph.D. in Economics from Texas A&M University in 2014. He is currently a Postdoctoral Research Associate at University of Southern California. His research fields are Econometric theory, time series analysis, panel data analysis and nonparametric econometrics. He is applying for a position of the Institute of Economics, Academia Sinica now.
演講摘要 : This paper considers estimation and inference in fixed effects (FE) panel regression models with lagged dependent variables and/or other weakly exogenous (or predetermined) regressors when N (the cross section dimension) is large relative to T (the time series dimension). The paper first derives a general formula for the bias of the FE estimator which is a generalization of the Nickell type bias derived in the literature for the pure dynamic panel data models. It shows that in the presence of weakly exogenous regressors, inference based on the FE estimator will result in size distortions unless N/T is sufficiently small. To deal with the bias and size distortion of FE estimator when N is large relative to T, the use of half-panel Jackknife FE estimator is proposed and its asymptotic distribution is derived. It is shown that the bias of the proposed estimator is of order T-2, and for valid inference it is only required that N/T3 →0, as N, T → ∞ jointly. Extensions to panel data models with time effects (TE), for balanced as well as unbalanced panels, are also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE estimator can suffer from large size distortions when N > T, with the proposed estimator showing little size distortions. The use of half-panel jackknife FE-TE estimator is illustrated with two empirical applications from the literature.
