The isotonic regression approach for an instrumental variable estimation of the potential outcome distributions for compliers

Byeong Yeob Choi and Jae Won Lee

Computational Statistics & Data Analysis, 2019, vol. 139, issue C, 134-144

Abstract: This paper discusses an instrumental variable estimation of the potential outcome distributions for compliers. The existing nonparametric estimators have a limitation in that they give non-proper cumulative distribution functions that violate the non-decreasing property. Using the least squares representation of the standard nonparametric estimators, a simple isotonic regression approach has been developed. A nonparametric bootstrap method is proposed as an appropriate method used to estimate the variances of the isotonic regression estimators. A simulation study demonstrates that the isotonic regression estimators provide more proper and efficient cumulative distribution functions, with much smaller standard errors than those of the standard nonparametric estimators when the proportion of compliers is small. The methods are illustrated with a study to estimate the distributional causal effect of a veteran status on future earnings.

Keywords: Compliers; Cumulative distribution functions; Instrumental variables; Isotonic regression (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947319300970
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:139:y:2019:i:c:p:134-144

DOI: 10.1016/j.csda.2019.04.013

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().