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

Speaker: Associate Professor Jiliang Shiu, Hanqing Advanced Institute of Economics and Finance, Renmin University of China

Time: July 25th, 14:00–15:00

Venue: Room 102, Zhonghui Building (College of Economics, JNU)

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. The result fills an important theoretic gap for the misspecification issue in random effect approaches. 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.