Statistics Seminar（2016-10）

Topic:Internally Consistent Estimation of Nonlinear Panel Data Models with Correlated Random Effects

Speaker:Ji-Liang Shiu, Renmin University of China

Time:Monday, 30 May, 14:00-15:30

Place:Room 219, Guanghua Building 1

Abstract:

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.

Introduction:

Dr. Ji-Liang Shiu received a Ph.D. in economics from the Johns Hopkins University in 2009 and had taught in National Chung-Cheng University and Renmin University of China. His research interest is in Microeconometrics, Labor Economy, Applied Econometrics, and Applied Microeconomics. Ji-Liang’s publication includes papers in Journal of Econometrics, Econometric Journal, and Empirical Economics, etc.

http://hanqing.ruc.edu.cn/faculty/JLShiu/

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