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Maximum Likelihood Estimation of the Panel Sample Selection Model

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Abstract

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.

Suggested Citation

  • Hung-pin Lai & Wen-Jen Tsay, 2012. "Maximum Likelihood Estimation of the Panel Sample Selection Model," IEAS Working Paper : academic research 12-A006, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Oct 2012.
  • Handle: RePEc:sin:wpaper:12-a006
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    Keywords

    Panel data; Sample selection; Maximum likelihood estimation; Closed skewed normal;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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