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Pré-Publication, Document De Travail Année : 2021

Nonparametric classes for identification in random coefficients models when regressors have limited variation

Christophe Gaillac
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Résumé

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.
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Dates et versions

hal-03231392 , version 1 (21-05-2021)

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Christophe Gaillac, Eric Gautier. Nonparametric classes for identification in random coefficients models when regressors have limited variation. 2021. ⟨hal-03231392⟩
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