Publication Type
Journal Article
Version
submittedVersion
Publication Date
10-2014
Abstract
In this article 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). Supplementary materials for this article are available online.
Keywords
Additive regression, Endogeneity, Generated regressors, Oracle estimation, Structural equation
Discipline
Econometrics | Economics
Research Areas
Econometrics
Publication
Journal of Business and Economic Statistics
Volume
32
Issue
4
First Page
555
Last Page
575
ISSN
0735-0015
Identifier
10.1080/07350015.2014.917590
Publisher
Taylor and Francis
Citation
OZABACI, Deniz; HENDERSON, Daniel J.; and SU, Liangjun.
Additive nonparametric regression in the presence of endogenous regressors. (2014). Journal of Business and Economic Statistics. 32, (4), 555-575.
Available at: https://ink.library.smu.edu.sg/soe_research/1634
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/07350015.2014.917590