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Adaptive estimation in the linear random coefficients model when regressors have limited variation

Gaillac, Christophe and Gautier, Eric (2019) Adaptive estimation in the linear random coefficients model when regressors have limited variation. TSE Working Paper, n. 19-1026, Toulouse

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Official URL : http://tse-fr.eu/pub/123181

Abstract

We consider a linear model where the coecients - intercept and slopes - are
random and independent from regressors which support is a proper subset. When the slopes do not have heavy tails, the joint density of the random coecients is identied. Lower bounds on the supremum risk for the estimation of the density are derived for this model and a related white noise model. We present an estimator, its rates of convergence, and a data-driven rule which delivers adaptive estimators.

Item Type: Monograph (Working Paper)
Language: English
Date: July 2019
Place of Publication: Toulouse
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse 1 Capitole
Site: UT1
Date Deposited: 19 Jul 2019 14:07
Last Modified: 25 May 2021 12:04
OAI Identifier: oai:tse-fr.eu:123181
URI: http://publications.ut-capitole.fr/id/eprint/32633

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