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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.

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  • 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. 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.
    3. Kossova, Elena & Potanin, Bogdan, 2018. "Heckman method and switching regression model multivariate generalization," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 114-143.
    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. 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.
    6. Fisher, Monica G. & Shively, Gerald E. & Buccola, Steven T., 2002. "An Empirical Investigation Of Activity Choice, Labor Allocation, And Forest Use In Southern Malawi," Staff Papers 28616, Purdue University, Department of Agricultural Economics.
    7. Thomas W. Zuehlke, 2017. "Use of quadratic terms in Type 2 Tobit models," Applied Economics, Taylor & Francis Journals, vol. 49(17), pages 1706-1714, April.
    8. 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, January.
    9. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    10. 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.
    11. 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.
    12. 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.
    13. Adrian (Waikong) Cheung & May Hu & Jörg Schwiebert, 2018. "Corporate social responsibility and dividend policy," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(3), pages 787-816, September.
    14. Hasebe, Takuya & Vijverberg, Wim P., 2012. "A Flexible Sample Selection Model: A GTL-Copula Approach," IZA Discussion Papers 7003, Institute of Labor Economics (IZA).

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