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

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  • Montes-Rojas, G.

Abstract

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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  • Montes-Rojas, G., 2008. "Robust misspecification tests for the Heckman’s two-step estimator," Working Papers 08/01, Department of Economics, City University London.
    • Handle: RePEc:cty:dpaper:08/01
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    Cited by:

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    3. Claudia PIGINI, 2012. "Of Butterflies and Caterpillars: Bivariate Normality in the Sample Selection Model," Working Papers 377, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    4. 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.
    5. Seonho Shin, 2022. "To work or not? Wages or subsidies?: Copula-based evidence of subsidized refugees’ negative selection into employment," Empirical Economics, Springer, vol. 63(4), pages 2209-2252, October.
    6. Riccardo LUCCHETTI & Claudia PIGINI, 2011. "Conditional Moment Tests for Normality in Bivariate Limited Dependent Variable Models: a Monte Carlo Study," Working Papers 357, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

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