Semiparametric estimation with generated covariates

Enno Mammen, Christoph Rothe and Melanie Schienle

No 81, Working Paper Series in Economics from Karlsruhe Institute of Technology (KIT), Department of Economics and Management

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 estimators are root-n consistent and asymptotically normal, derive a general formula for the asymptotic variance, and show how to establish validity of the bootstrap.

Keywords: semiparametric estimation; generated covariates; profiling; propensity score (search for similar items in EconPapers)
JEL-codes: C14 C31 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (23)

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Related works:
Journal Article: SEMIPARAMETRIC ESTIMATION WITH GENERATED COVARIATES (2016)
Working Paper: Semiparametric Estimation with Generated Covariates (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:kitwps:81

DOI: 10.5445/IR/1000051816

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