Two-Step Estimation, Optimal Moment Conditions, and Sample Selection Models

Posted: 26 Jul 2000

See all articles by Whitney K. Newey

Whitney K. Newey

Massachusetts Institute of Technology (MIT) - Department of Economics; National Bureau of Economic Research (NBER)

James L. Powell

University of California, Berkeley

Date Written: February 1999

Abstract

Two step estimators with a nonparametric first step are important, particularly for sample selection models where the first step is estimation of the propensity score. In this paper we consider the efficiency of such estimators. We characterize the efficient moment condition for a given first step nonparametric estimator. We also show how it is possible to approximately attain efficiency by combining many moment conditions. In addition we find that the efficient moment condition often leads to an estimator that attains the semiparametric efficiency bound. As illustrations we consider models with expectations and semiparametric minimum distance estimation.

Keywords: Efficiency, Two-Step Estimation, Sample Selection Models, Semiparametric Estimation

JEL Classification: C10, C21

Suggested Citation

Newey, Whitney K. and Powell, James L., Two-Step Estimation, Optimal Moment Conditions, and Sample Selection Models (February 1999). Available at SSRN: https://ssrn.com/abstract=235808

Whitney K. Newey (Contact Author)

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

50 Memorial Drive
E52-262D
Cambridge, MA 02142
United States
617-253-6420 (Phone)

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

James L. Powell

University of California, Berkeley

310 Barrows Hall
Berkeley, CA 94720
United States

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