Nonparametric estimation in case of endogenous selection

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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Hauptverfasser: Breunig, Christoph (VerfasserIn) , Mammen, Enno (VerfasserIn) , Simoni, Anna (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2018
In: Journal of econometrics
Year: 2018, Jahrgang: 202, Heft: 2, Pages: 268-285
DOI:10.1016/j.jeconom.2017.11.002
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.1016/j.jeconom.2017.11.002
Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S0304407617302221
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. 
650 4 |a Asymptotic normality 
650 4 |a Bootstrap uniform confidence bands 
650 4 |a Convergence rate 
650 4 |a Endogenous selection 
650 4 |a Instrumental variable 
650 4 |a Inverse probability weighting 
650 4 |a Inverse problem 
650 4 |a Regression estimation 
650 4 |a Sieve minimum distance 
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