Additive Nonparametric Regression in the Presence of Endogenous Regressors
Daniel Henderson () and
Journal of Business & Economic Statistics, 2014, vol. 32, issue 4, 555-575
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.
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
Working Paper: Additive Nonparametric Regression in the Presence of Endogenous Regressors (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:32:y:2014:i:4:p:555-575
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan
More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().