Nonparametric classes for identification in random coefficients models when regressors have limited variation
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21 mai 2021
- handle: 10670/1.qm7vvy
- hal: hal-03231392
- ARXIV: 2105.11720
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info:eu-repo/semantics/altIdentifier/arxiv/2105.11720
Ce document est lié à :
info:eu-repo/grantAgreement/EC/FP7/337665/EU/Parsimony and operator methods for treatment of endogeneity and multiple sources of unobserved heterogeneity/POEMH
info:eu-repo/semantics/OpenAccess
Mots-clés
Identification Random Coefficients Quasi-analyticity Deconvolution Deconvolution AMS 2010 Subject Classification: Primary 62P20 secondary 42A99 62G07 62G08 Identification 62P20, 42A99, 62G07, 62G08.Sujets proches
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Christophe Gaillac et al., « Nonparametric classes for identification in random coefficients models when regressors have limited variation », HAL-SHS : économie et finance, ID : 10670/1.qm7vvy
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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.