Semiparametric Estimation of a Sample Selection Model: A Simulation Study

Marcia M Schafgans

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

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.

Keywords: sample selection models; semiparametric estimation; error distributions; bandwidth parameter; two-step parametric estimator. (search for similar items in EconPapers)
Date: 1997-03
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:326

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