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Nonparametric estimation in case of endogenous selection

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This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for....

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Bibliographische Detailangaben
Hauptverfasser: Breunig, Christoph (VerfasserIn) , Mammen, Enno (VerfasserIn) , Simoni, Anna (VerfasserIn)
Dokumenttyp: Buch/Monographie Arbeitspapier
Sprache:Englisch
Veröffentlicht: Munich, Germany Collaborative Research Center Transregio 190 2017
Schriftenreihe:Discussion paper no. 58 (December 20, 2017)
In: Discussion paper (no. 58 (December 20, 2017))

Schlagworte:
Graue Literatur
Online-Zugang:Resolving-System, kostenfrei, Volltext: http://hdl.handle.net/10419/185728
Verlag, kostenfrei, Volltext: https://rationality-and-competition.de/wp-content/uploads/discussion_paper/58.pdf
Volltext
Verfasserangaben:Christoph Breunig, Enno Mammen, Anna Simoni

MARC

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520 |a This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for. In the exogenous and endogenous case, consistent two-step estimation procedures are proposed and their rates of convergence are derived. Pointwise asymptotic distribution of the estimators is established. In addition, bootstrap uniform confidence bands are obtained. Finite sample properties are illustrated in a Monte Carlo simulation study and an empirical illustration. 
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