Gaillac, Christophe and Gautier, Éric (2021) Non Parametric Classes for Identification in Random Coefficients Models when Regressors have Limited Variation. TSE Working Paper, n. 21-1218, Toulouse

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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.