Semiparametric estimation with generated covariates
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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| Main Authors: | , , |
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| Format: | Book/Monograph Working Paper |
| Language: | English |
| Published: |
Bonn
IZA
2011
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| Series: | Discussion paper series / Forschungsinstitut zur Zukunft der Arbeit
6084 |
| In: |
Discussion paper series (6084)
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| Subjects: | |
| Online Access: | 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 
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| Author Notes: | Enno Mammen; Christoph Rothe; Melanie Schienle |
| Summary: | 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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| Physical Description: | Online Resource |
| Format: | Systemvoraussetzung: Acrobat Reader. |