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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| Main Authors: | , , |
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| Format: | Book/Monograph Working Paper |
| Language: | English |
| Published: |
Berlin
SFB 649, Economic Risk
2015
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| Series: | SFB 649 discussion paper
2015-050 |
| In: |
SFB 649 discussion paper (2015-050)
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| Subjects: | |
| Online Access: | Resolving-System, Volltext: http://hdl.handle.net/10419/146165 Verlag, Volltext: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2015-050.pdf 
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| Author Notes: | Christoph Breunig; Enno Mammen; Anna Simoni |
| Summary: | 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. |
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| Physical Description: | Online Resource |