Nonparametric identification of a binary random factor in cross section data

A-Tier
Journal: Journal of Econometrics
Year: 2011
Volume: 163
Issue: 2
Pages: 163-171

Authors (2)

Dong, Yingying (not in RePEc) Lewbel, Arthur (Boston College)

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

Suppose V and U are two independent mean zero random variables, where V has an asymmetric distribution with two mass points and U has some zero odd moments (having a symmetric distribution suffices). We show that the distributions of V and U are nonparametrically identified just from observing the sum V+U, and provide a pointwise rate root n estimator. This can permit point identification of average treatment effects when the econometrician does not observe who was treated. We extend our results to include covariates X, showing that we can nonparametrically identify and estimate cross section regression models of the form Y=g(X,D*)+U, where D* is an unobserved binary regressor.

Technical Details

RePEc Handle
repec:eee:econom:v:163:y:2011:i:2:p:163-171
Journal Field
Econometrics
Author Count
2
Added to Database
2026-01-25