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: |
Munich, Germany
Collaborative Research Center Transregio 190
2017
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| Series: | Discussion paper
no. 58 (December 20, 2017) |
| In: |
Discussion paper (no. 58 (December 20, 2017))
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| Subjects: | |
| Online Access: | 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 
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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 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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| Physical Description: | Online Resource |