Semiparametric Estimation with Generated Covariates

44 Pages Posted: 13 Nov 2011

See all articles by Enno Mammen

Enno Mammen

University of Mannheim - Department of Economics

Christoph Rothe

Columbia University

Melanie Schienle

Humboldt University of Berlin - School of Business and Economics

Abstract

In this paper, we study a general class of semiparametric optimization estimators of a vector-valued parameter. The criterion function depends on two types of infinite-dimensional nuisance parameters: a conditional expectation function that has been estimated nonparametrically using generated covariates, and another estimated function that is used to compute the generated covariates in the first place. We study the asymptotic properties of estimators in this class, which is a nonstandard problem due to the presence of generated covariates. We give conditions under which estimators are root-n consistent and asymptotically normal, and derive a general formula for the asymptotic variance.

Keywords: semiparametric estimation, generated covariates, profiling, propensity score

JEL Classification: C14, C31

Suggested Citation

Mammen, Enno and Rothe, Christoph and Schienle, Melanie, Semiparametric Estimation with Generated Covariates. IZA Discussion Paper No. 6084, Available at SSRN: https://ssrn.com/abstract=1958739 or http://dx.doi.org/10.2139/ssrn.1958739

Enno Mammen (Contact Author)

University of Mannheim - Department of Economics ( email )

Mannheim, 68131
Germany

Christoph Rothe

Columbia University ( email )

Melanie Schienle

Humboldt University of Berlin - School of Business and Economics ( email )

Spandauer Str. 1
Berlin, D-10099
Germany

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