%S TSE Working Paper
%A Christophe Gaillac
%A Éric Gautier
%T Non Parametric Classes for Identification in Random Coefficients Models when Regressors have Limited Variation
%X 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.
%K Identification
%K Random Coefficients
%K Quasi-analyticity
%K Deconvolution
%B TSE Working Paper
%V 21-1218
%D 2021
%C Toulouse
%I TSE Working Paper
%L publications43568