Maximum Likelihood Estimation of the Dynamic Panel Sample Selection Model
2012/09/18
研討會日期 : 2012-09-18
時間 : 14:30
主講人 : Professor Hung-Pin Lai
地點 : Conference Room B110
演講者簡介 : Hung-Pin Lai received her Ph.D. in Economics from Penn State University in 2003. She is currently serving as Associate Professor of Economics at National Chung Cheng University. Her research field is in econometrics and macroeconomics.
演講摘要 : Heckman’s (1976, 1979) sample selection model has been employed in many studies of liner or nonlinear regression applications. It is well known that ignoring the sample selectivity problem may result in inconsistency of the estimator due to the correlation between the statistical errors in the selection and main equations. In this paper, we consider the problem of estimating a dynamic panel sample selection model. Since the dynamic panel data model contains the individual effects, such as the fixed or random effect, the likelihood function is quite complicated when the sample selection is taken into account. We therefore propose to solve the estimation problem by utilizing the maximum likelihood (ML) approach together with the closed skewed normal distribution. Finally, we also conduct a Monte Carlo experiment to investigate the finite sample performance of the proposed estimator and find that our ML estimator provides reliable and quite satisfactory results.
