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Nonparametric identification of a binary random factor in cross section data

Author

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  • Yingyong Dong

    (Institute for Fiscal Studies)

  • Arthur Lewbel

    () (Institute for Fiscal Studies and 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.

Suggested Citation

  • Yingyong Dong & Arthur Lewbel, 2009. "Nonparametric identification of a binary random factor in cross section data," CeMMAP working papers CWP16/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:16/09
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    References listed on IDEAS

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    Cited by:

    1. Dong, Yingying, 2011. "Semiparametric binary random effects models: Estimating two types of drinking behavior," Economics Letters, Elsevier, vol. 112(1), pages 79-81, July.
    2. repec:eee:econom:v:202:y:2018:i:2:p:148-160 is not listed on IDEAS
    3. Park, Byeong U. & Simar, LĂ©opold & 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.

    More about this item

    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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