Quantile Regression with Classical Additive Measurement Errors

This note derives the bias of the quantile regression estimator in the presence of classical additive measurement error, and show its connection to least squares models. The bias structure suggests that the instrumental variables estimator proposed for least squares can be applied to the quantile regression case.

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Year of publication:	2011
Authors:	Montes-Rojas, Gabriel
Published in:	Economics Bulletin. - AccessEcon, ISSN 1545-2921. - Vol. 31.2011, 4, p. 2863-2868
Publisher:	AccessEcon
Subject:	Quantile Regression \| Measurement Errors \| Instrumental Variables

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Extent:	application/pdf
Type of publication:	Article
Language:	English
Classification:	C1 - Econometric and Statistical Methods: General ; C2 - Econometric Methods: Single Equation Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009320381