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Nonparametric Identification of a Binary Random Factor in Cross Section Data

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  • Yingying Dong

    (California State University, Fullerton)

  • Arthur Lewbel

    ()
    (Boston College)

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 a symmetric distribution. We show that the distributions of V and U are nonparametrically identified just from observing the sum V+U, and provide a rate root n estimator. We apply these results to the world income distribution to measure the extent of convergence over time, where the values V can take on correspond to country types, i.e., wealthy versus poor countries. We also 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

Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 707.

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Date of creation: 16 Jun 2009
Date of revision: 01 Jul 2010
Handle: RePEc:boc:bocoec:707

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

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