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Non Parametric Classes for Identification in Random Coefficients Models when Regressors have Limited Variation

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  • Gaillac, Christophe
  • Gautier, Eric
Abstract
This paper studies point identification of the distribution of the coefficients in some random coefficients models with exogenous regressors when their support is a proper subset, possibly discrete but countable. We exhibit trade-offs between restrictions on the distribution of the random coefficients and the support of the regressors. We consider linear models including those with nonlinear transforms of a baseline regressor, with an infinite number of regressors and deconvolution, the binary choice model, and panel data models such as single-index panel data models and an extension of the Kotlarski lemma.

Suggested Citation

  • Gaillac, Christophe & Gautier, Eric, 2021. "Non Parametric Classes for Identification in Random Coefficients Models when Regressors have Limited Variation," TSE Working Papers 21-1218, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:125629
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    References listed on IDEAS

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    Keywords

    Identification; Random Coefficients; Quasi-analyticity; Deconvolution;
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