Nonparametric estimation in case of endogenous selection

Christoph Breunig, Enno Mammen and Anna Simoni

No 2015-050, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: endogenous selection; instrumental variable; sieve minimum distance; regression estimation; convergence rate; asymptotic normality; hypothesis testing; inverse problem (search for similar items in EconPapers)
JEL-codes: C14 C26 (search for similar items in EconPapers)
Date: 2015
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Related works:
Journal Article: Nonparametric estimation in case of endogenous selection (2018)
Working Paper: Nonparametric estimation in case of endogenous selection (2018)
Working Paper: Nonparametric Estimation in Case of Endogenous Selection (2017)
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