Internally Consistent Estimation of Nonlinear Panel Data Models with Correlated Random Effects
2016/04/29
研討會日期 : 2016-04-29
時間 : 10:30
主講人 : Professor Ji-Liang Shiu
主持人 : Professor Yu-Chin Hsu
地點 : Conference Room B110
演講者簡介 : Prof. Shiu received his Ph.D. in Economics from Johns Hopkins University and Ph.D. in Mathematics from Indiana University at Bloomington. He is currently an Associate Professor at Hanqing Advanced Institute of Economics and Finance, Renmin University of China. His research fields are Micro-econometrics, Applied Econometrics, Applied Microeconomics, and Labor Economics.
演講摘要 : This paper investigates identification and estimation of parametric nonlinear panel data models with correlated unobserved effects. It is shown under the Mundlak-type specification, a conditional distribution of the unobserved heterogeneity can be recovery by means of Fourier inversion formula. Combining the proposed panel data models with the conditional distribution, we can construct a parametric family of average likelihood functions of observables and then the parameter vector is identifiable by the negative definiteness of the information matrix. Based on the identification condition, we propose a semiparametric two-step maximum likelihood estimator which is root n consistent and asymptotically normal. The finite-sample properties of the estimator are investigated through Monte Carlo simulations.
