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Semiparametric Estimation of a Sample Selection Model: A Simulation Study

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  • Marcia M Schafgans

Abstract

Standard approaches to the estimation of sample selection models are known to be inconsistent under non-normality. In particular, this paper considers the two-step Heckman (1976, 1979) estimator of the interecept of the outcome equation. This estimator is compared with a consistent asymptotically normal semiparametric estimator suggested by Andrews and Schafgans (1996). Using a root mean squared error criterion, the semiparametric estimator performs better for a range of bandwidth parameter choice for a variety of distributions of the errors and regressors. For error distributions that are close to the normal, however, the two-step parametric estimator performs better.

Suggested Citation

  • Marcia M Schafgans, 1997. "Semiparametric Estimation of a Sample Selection Model: A Simulation Study," STICERD - Econometrics Paper Series 326, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:326
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    Cited by:

    1. Schafgans, Marcia M. A., 2000. "Gender wage differences in Malaysia: parametric and semiparametric estimation," Journal of Development Economics, Elsevier, vol. 63(2), pages 351-378, December.
    2. Marcia M. A. Schafgans, 2004. "Finite sample properties for the semiparametric estimation of the intercept of a censored regression model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(1), pages 35-56, February.
    3. Ana Fernandez Sainz & Juan Rodriguez-Poo & Inmaculada Villanua Martin, 2002. "Finite sample behavior of two step estimators in selection models," Computational Statistics, Springer, vol. 17(1), pages 1-16, March.

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