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Robust misspecification tests for the Heckman’s two-step estimator

  • Montes-Rojas, G.

We construct and evaluate LM and Neyman’s C(α) tests based on bivariate Edgeworth expansions for the consistency of the Heckman’s two-step estimator in selection models, that is, for the marginal normality and linearity of the conditional expectation of the error terms. The proposed tests are robust to local misspecification in nuisance distributional parameters. Monte Carlo results show that instead of testing bivariate normality, testing marginal normality and linearity of the conditional expectations separately have a better size performance. Moreover, the robust variants of the tests have better size and similar power to non-robust tests, which determines that these tests can be successfully applied to detect specific departures from the null model of bivariate normality. We apply the tests procedures to women’s labor supply data.

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Paper provided by Department of Economics, City University London in its series Working Papers with number 08/01.

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Date of creation: 2008
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Handle: RePEc:cty:dpaper:08/01
Contact details of provider: Postal: Department of Economics, Social Sciences Building, City University London, Whiskin Street, London, EC1R 0JD, United Kingdom,
Phone: +44 (0)20 7040 8500
Web page: http://www.city.ac.uk

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  1. James Heckman & Justin L. Tobias & Edward Vytlacil, 2003. "Simple Estimators for Treatment Parameters in a Latent-Variable Framework," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 748-755, August.
  2. repec:att:wimass:9001 is not listed on IDEAS
  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 563-78, October.
  4. Jaggia, Sanjiv & Trivedi, Pravin K., 1994. "Joint and separate score tests for state dependence and unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 273-291.
  5. Lee, Lung-Fei, 1984. "Tests for the Bivariate Normal Distribution in Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 52(4), pages 843-63, July.
  6. Lee, Lung-Fei, 1982. "Some Approaches to the Correction of Selectivity Bias," Review of Economic Studies, Wiley Blackwell, vol. 49(3), pages 355-72, July.
  7. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-90, March.
  8. Gabler, Siegfried & Laisney, Francois & Lechner, Michael, 1993. "Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 61-80, January.
  9. Moshe Buchinsky, 1998. "The dynamics of changes in the female wage distribution in the USA: a quantile regression approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 1-30.
  10. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-28, May.
  11. Bera, Anil K. & Yoon, Mann J., 1993. "Specification Testing with Locally Misspecified Alternatives," Econometric Theory, Cambridge University Press, vol. 9(04), pages 649-658, August.
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