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Nonparametric identification of a binary random factor in cross section data
被引:8
|作者:
Dong, Yingying
Lewbel, Arthur
[1
]
机构:
[1] Boston Coll, Dept Econ, Chestnut Hill, MA 02467 USA
关键词:
Mixture model;
Random effects;
Binary;
Unobserved factor;
Unobserved regressor;
Nonparametric identification;
Deconvolution;
Treatment;
LEAST-SQUARES ESTIMATION;
GENERALIZED-METHOD;
REGRESSION-MODELS;
GMM;
D O I:
10.1016/j.jeconom.2011.03.003
中图分类号:
F [经济];
学科分类号:
02 ;
摘要:
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. (C) 2011 Elsevier B.V. All rights reserved.
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页码:163 / 171
页数:9
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