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Additive Nonparametric Regression in the Presence of Endogenous Regressors

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).
Year of publication:
2014-04
Authors: Ozabaci, Deniz ; Henderson, Daniel J. ; Su, Liangjun
Institutions: Institute for the Study of Labor (IZA)
Subject: child care | structural equation | additive regression | endogeneity | generated regressors | oracle estimation | nonparametric regression | test scores
Extent:
application/pdf
Series:
Type of publication: Book / Working Paper
Notes:
published in: Journal of Business and Economic Statistics, 2014, 32, 555-575 Number 8144 53 pages
Classification: C14 - Semiparametric and Nonparametric Methods ; c36 ; I21 - Analysis of Education ; J13 - Fertility; Family Planning; Child Care; Children; Youth
Source:
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011105994
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