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Semiparametric estimation with generated covariates

Mammen, Enno; Rothe, Christoph; Schienle, Melanie ORCID iD icon

Abstract:

We study a general class of semiparametric estimators when the infinite-dimensional nuisance parameters include a conditional expectation function that has been estimated nonparametrically using generated covariates. Such estimators are used frequently to e.g. estimate nonlinear models with endogenous covariates when identification is achieved using control variable techniques. We study the asymptotic properties of estimators in this class, which is a non-standard problem due to the presence of generated covariates. We give conditions under which estimatorsare root-n consistent and asymptotically normal, derive a general formula for the asymptotic variance, and show how to establish validity of the bootstrap.


Volltext §
DOI: 10.5445/IR/1000051816
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2016
Sprache Englisch
Identifikator ISSN: 2190-9806
urn:nbn:de:swb:90-518162
KITopen-ID: 1000051816
Verlag Karlsruher Institut für Technologie (KIT)
Umfang 43 S.
Serie Working paper series in economics ; 81
Schlagwörter Semiparametric estimation, generated covariates, profiling, propensity score
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