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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.

Suggested Citation

  • Dong, Yingying & Lewbel, Arthur, 2011. "Nonparametric identification of a binary random factor in cross section data," Journal of Econometrics, Elsevier, vol. 163(2), pages 163-171, August.
    • Handle: RePEc:eee:econom:v:163:y:2011:i:2:p:163-171
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    Cited by:

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    3. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 97-120.
    4. Dong, Yingying, 2011. "Semiparametric binary random effects models: Estimating two types of drinking behavior," Economics Letters, Elsevier, vol. 112(1), pages 79-81, July.

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    More about this item

    Keywords

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

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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