Semiparametric Estimation with Generated Covariates

Enno Mammen (), Christoph Rothe and Melanie Schienle ()
Additional contact information
Enno Mammen: Heidelberg University
Melanie Schienle: Humboldt University Berlin

No 6084, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 (search for similar items in EconPapers)
JEL-codes: C14 C31 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2011-10
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Published - published in: Econometric Theory, 2016, 32 (5), 1140-1177

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Related works:
Journal Article: SEMIPARAMETRIC ESTIMATION WITH GENERATED COVARIATES (2016)
Working Paper: Semiparametric estimation with generated covariates (2016)
Working Paper: Semiparametric Estimation with Generated Covariates (2014)
Working Paper: Semiparametric estimation with generated covariates (2011)
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