@techreport{publications43568,
          volume = {21-1218},
           month = {May},
          author = {Christophe Gaillac and {\'E}ric Gautier},
          series = {TSE Working Paper},
       booktitle = {TSE Working Paper},
            type = {Working Paper},
         address = {Toulouse},
           title = {Non Parametric Classes for Identification in Random Coefficients Models when Regressors have Limited Variation},
       publisher = {TSE Working Paper},
            year = {2021},
     institution = {Universit{\'e} Toulouse 1 Capitole},
        keywords = {Identification, Random Coefficients, Quasi-analyticity, Deconvolution},
             url = {https://publications.ut-capitole.fr/id/eprint/43568/},
        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.}
}