Maximum Likelihood Estimation of the Panel Sample Selection Model
Heckman’s (1976, 1979) sample selection model has been employed in many studies of linear 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 panel sample selection model. Since the 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.
|Date of creation:||Aug 2012|
|Date of revision:||Oct 2012|
|Contact details of provider:|| Phone: 886-2-27822791|
Web page: http://www.econ.sinica.edu.tw/index.php?foreLang=en
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:sin:wpaper:12-a006. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (HsiaoyunLiu)
If references are entirely missing, you can add them using this form.