Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/185728
Authors: 
Breunig, Christoph
Mammen, Enno
Simoni, Anna
Year of Publication: 
2017
Series/Report no.: 
Discussion Paper No. 58
Abstract: 
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.
Subjects: 
endogenous selection
instrumental variable
sieve minimum distance
regression estimation
inverse problem
inverse probability weighting
convergence rate
asymptotic normality
bootstrap uniform confidence bands
JEL: 
C14
C26
Document Type: 
Working Paper

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