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Semiparametric estimation with generated covariates

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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 covari...

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Bibliographische Detailangaben
Hauptverfasser: Mammen, Enno (VerfasserIn) , Rothe, Christoph (VerfasserIn) , Schienle, Melanie (VerfasserIn)
Dokumenttyp: Book/Monograph Arbeitspapier
Sprache:Englisch
Veröffentlicht: Bonn IZA 2011
Schriftenreihe:Discussion paper series / Forschungsinstitut zur Zukunft der Arbeit 6084
In: Discussion paper series (6084)

Schlagworte:
Arbeitspapier
Graue Literatur
Online-Zugang:Resolving-System, Volltext, Volltext: http://nbn-resolving.de/urn:nbn:de:101:1-201111213059
Verlag, Volltext: http://ftp.iza.org/dp6084.pdf
Download aus dem Internet, Stand: 05.04.2012, Volltext: http://hdl.handle.net/10419/58774
Volltext
Verfasserangaben:Enno Mammen; Christoph Rothe; Melanie Schienle

MARC

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520 |a 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. -- semiparametric estimation ; generated covariates ; profiling ; propensity score 
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