Additive Nonparametric Regression in the Presence of Endogenous Regressors

Deniz Ozabaci, Daniel Henderson () and Liangjun Su ()

No 8144, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: In this paper we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient and free from the curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and cognitive outcomes. Some of our (average) results are consistent with the literature (e.g., negative returns to child care when mothers have higher levels of education). However, as our estimators allow for heterogeneity both across and within groups, we are able to contradict many findings in the literature (e.g., we do not find any significant differences in returns between boys and girls or for formal versus informal child care).

Keywords: child care; structural equation; additive regression; endogeneity; generated regressors; oracle estimation; nonparametric regression; test scores (search for similar items in EconPapers)
JEL-codes: C14 C36 I21 J13 (search for similar items in EconPapers)
Pages: 53 pages
Date: 2014-04
New Economics Papers: this item is included in nep-ecm and nep-sea
References: View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Published - published in: Journal of Business and Economic Statistics, 2014, 32, 555-575

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Journal Article: Additive Nonparametric Regression in the Presence of Endogenous Regressors (2014)
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