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A GMM-Based Test for Normal Disturbances of the Heckman Sample Selection Model

Author

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  • Michael Pfaffermayr

    () (Department of Economics, University of Innsbruck, Universitaetsstrasse 15, Innsbruck 6020, Austria
    Austrian Institute of Economic Research, P.O.-Box 91, Vienna A-1103, Austria)

Abstract

The Heckman sample selection model relies on the assumption of normal and homoskedastic disturbances. However, before considering more general, alternative semiparametric models that do not need the normality assumption, it seems useful to test this assumption. Following Meijer and Wansbeek (2007), the present contribution derives a GMM-based pseudo-score LM test on whether the third and fourth moments of the disturbances of the outcome equation of the Heckman model conform to those implied by the truncated normal distribution. The test is easy to calculate and in Monte Carlo simulations it shows good performance for sample sizes of 1000 or larger.

Suggested Citation

  • Michael Pfaffermayr, 2014. "A GMM-Based Test for Normal Disturbances of the Heckman Sample Selection Model," Econometrics, MDPI, Open Access Journal, vol. 2(4), pages 1-18, October.
    • Handle: RePEc:gam:jecnmx:v:2:y:2014:i:4:p:151-168:d:41573
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    References listed on IDEAS

    as
    1. Gabriel Montes-Rojas, 2011. "Robust Misspecification Tests for the Heckman's Two-Step Estimator," Econometric Reviews, Taylor & Francis Journals, vol. 30(2), pages 154-172.
    2. Newey, Whitney K & West, Kenneth D, 1987. "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 777-787, October.
    3. Bera, Anil K & Jarque, Carlos M & Lee, Lung-Fei, 1984. "Testing the Normality Assumption in Limited Dependent Variable Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(3), pages 563-578, October.
    4. Kevin E. Staub, 2014. "A Causal Interpretation of Extensive and Intensive Margin Effects in Generalized Tobit Models," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 371-375, May.
    5. Lee, Lung-Fei, 1984. "Tests for the Bivariate Normal Distribution in Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 52(4), pages 843-863, July.
    6. van der Klaauw, Bas & Koning, Ruud H, 2003. "Testing the Normality Assumption in the Sample Selection Model with an Application to Travel Demand," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 31-42, January.
    7. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    8. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    9. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    10. Yen, Steven T. & Rosinski, Jan, 2008. "On the marginal effects of variables in the log-transformed sample selection models," Economics Letters, Elsevier, vol. 100(1), pages 4-8, July.
    11. Skeels, Christopher L. & Vella, Francis, 1999. "A Monte Carlo investigation of the sampling behavior of conditional moment tests in Tobit and Probit models," Journal of Econometrics, Elsevier, vol. 92(2), pages 275-294, October.
    12. Erik Meijer & Tom Wansbeek, 2007. "The Sample Selection Model from a Method of Moments Perspective," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 25-51.
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    More about this item

    Keywords

    sample selection model; GMM; normality; pseudo-score LM test;

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C - Mathematical and Quantitative Methods
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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