Gaillac, Christophe and Gautier, Eric (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.

Item Type: Monograph (Working Paper)
Language: English
Date: May 2021
Place of Publication: Toulouse
Uncontrolled Keywords: Identification, Random Coefficients, Quasi-analyticity, Deconvolution
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse 1 Capitole
Site: UT1
Date Deposited: 25 May 2021 13:18
Last Modified: 01 Sep 2021 09:14
OAI Identifier: oai:tse-fr.eu:125629
URI: https://publications.ut-capitole.fr/id/eprint/43568
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