Internally Consistent Estimation of Nonlinear Panel Data Models with Correlated Random Effects
报告人： Ji-Liang Shiu, Renmin University of China
时间：2016-05-30 14:00 ~ 15:00
地点：Room 219, Guanghua Building 1
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.
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