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Nonparametric identification of a binary random factor in cross section data

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  • Dong, Yingying
  • Lewbel, Arthur

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 163 (2011)
Issue (Month): 2 (August)
Pages: 163-171

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Handle: RePEc:eee:econom:v:163:y:2011:i:2:p:163-171

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Mixture model Random effects Binary Unobserved factor Unobserved regressor Nonparametric identification Deconvolution Treatment;

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  1. Li, Qi & Racine, Jeff, 2003. "Nonparametric estimation of distributions with categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 266-292, August.
  2. Dong, Yingying, 2010. "Semiparametric Binary Random Effects Models: Estimating Two Types of Drinking Behavior," MPRA Paper 25425, University Library of Munich, Germany.
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