Finite sample properties for the semiparametric estimation of the intercept of a censored regression model

Marcia M. A. Schafgans

Statistica Neerlandica, 2004, vol. 58, issue 1, 35-56

Abstract: Financial support for this paper was provided by a C.A. Anderson Fellowship of the Cowles Foundation. I wish to thank Donald Andrews, Moshe Buchinsky, Oliver Linton, and Peter Robinson for helpful discussions. I also wish to thank three anonymous referees for their comments and suggestions. I am, of course, responsible for any remaining errors. A popular two‐step estimator of the intercept of a censored regression model is compared with consistent asymptotically normal semiparametric alternatives. Using a root mean squared error criterion, the semiparametric estimators perform better for a range of bandwidth parameter choices 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.

Date: 2004
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