Nonparametric regression with nonparametrically generated covariates

We analyze the properties of non- and semiparametric estimation procedures involving nonparametric regression with generated covariates. Such estimators appear in numerous econometric applications, including nonparametric estimation of simultaneous equation models, sample selection models, treatment...

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Bibliographische Detailangaben
Hauptverfasser: Mammen, Enno (VerfasserIn) , Rothe, Christoph (VerfasserIn) , Schienle, Melanie (VerfasserIn)
Dokumenttyp: Book/Monograph Arbeitspapier
Sprache:Englisch
Veröffentlicht: Berlin SFB 649, Economic Risk 2010
Ausgabe:This version: September 2010
Schriftenreihe:SFB 649 discussion paper 2010-059
In: SFB 649 discussion paper (2010-059)

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Online-Zugang:Verlag, Volltext: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2010-059.pdf
Download aus dem Internet, Stand: 14.12.2010, Volltext: http://hdl.handle.net/10419/56748
Volltext
Verfasserangaben:Enno Mammen; Christoph Rothe; Melanie Schienle
Beschreibung
Zusammenfassung:We analyze the properties of non- and semiparametric estimation procedures involving nonparametric regression with generated covariates. Such estimators appear in numerous econometric applications, including nonparametric estimation of simultaneous equation models, sample selection models, treatment effect models, and censored regression models, but so far there seems to be no unified theory to establish their statistical properties. Our paper provides such results, allowing to establish asymptotic properties like rates of consistency or asymptotic normality for a wide range of semi- and nonparametric estimators. We also show how to account for the presence of nonparametrically generated regressors when computing standard errors. -- Empirical Process ; Propensity Score ; Control Variable Methods ; Semiparametric Estimation
Beschreibung:Online Resource
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