Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/146165
Authors: 
Breunig, Christoph
Mammen, Enno
Simoni, Anna
Year of Publication: 
2015
Series/Report no.: 
SFB 649 Discussion Paper 2015-050
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 both cases, consistent two-step estimation procedures are proposed and their rates of convergence are derived. Also pointwise asymptotic distribution of the estimators is established. In addition, we propose a nonparametric specification test to check the validity of our independence assumption. 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
convergence rate
asymptotic normality
hypothesis testing
inverse problem
JEL: 
C14
C26
Document Type: 
Working Paper

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