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A Comparison of Two-Stage Estimators of Censored Regression Models

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  • Zuehlke, Thomas W
  • Zeman, Allen R

Abstract

This paper presents a Monte Carlo comparison of the small-sample performance of subsample ordinary least squares, the Heckman-Lee two-stage estimator, and the robust estimator of Lee. Each estimator is considered under bivariate normal, t, and chi-square error structures. The estimates indicate that the Heckman-Lee and Lee estimators do not provide an unequivocal mean square error improvement upon subsample ordinary least squares in small samples. While effectively controlling for selectivity bias, the two-stage estimators suffer a substantial loss of small-sample precision relative to subsample ordinary least squares. Copyright 1991 by MIT Press.

Suggested Citation

  • Zuehlke, Thomas W & Zeman, Allen R, 1991. "A Comparison of Two-Stage Estimators of Censored Regression Models," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 185-188, February.
  • Handle: RePEc:tpr:restat:v:73:y:1991:i:1:p:185-88
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    Cited by:

    1. Campbell, Randall C. & Nagel, Gregory L., 2016. "Private information and limitations of Heckman's estimator in banking and corporate finance research," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 186-195.
    2. Rochelle Belkar & Denzil G. Fiebig & Marion Haas & Rosalie Viney, 2006. "Why worry about awareness in choice problems? Econometric analysis of screening for cervical cancer," Health Economics, John Wiley & Sons, Ltd., vol. 15(1), pages 33-47.
    3. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    4. Belkar, R. & Fiebig, D.G., 2008. "A Monte Carlo comparison of estimators for a bivariate probit model with selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 250-256.
    5. Lee C. Adkins, 2008. "Small Sample Performance of Instrumental Variables Probit Estimators: A Monte Carlo Investigation," Economics Working Paper Series 0807, Oklahoma State University, Department of Economics and Legal Studies in Business.
    6. Mikhail Zhelonkin & Marc G. Genton & Elvezio Ronchetti, 2016. "Robust inference in sample selection models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 805-827, September.
    7. Yang Li & Walter J. Mayer, 2007. "Impact of corrections for dynamic selection bias on forecasts of retention behavior," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 571-582.
    8. Hasebe, Takuya & Vijverberg, Wim P., 2012. "A Flexible Sample Selection Model: A GTL-Copula Approach," IZA Discussion Papers 7003, Institute for the Study of Labor (IZA).
    9. Lee Adkins & R. Carter Hill, 2007. "Bootstrap Inferences in Heteroscedastic Sample Selection Models: A Monte Carlo Investigation," Economics Working Paper Series 0710, Oklahoma State University, Department of Economics and Legal Studies in Business.
    10. Ulrich Rendtel, 1992. "On the Choice of a Selection-Model When Estimating Regressionmodels with Selectivity," Discussion Papers of DIW Berlin 53, DIW Berlin, German Institute for Economic Research.

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