Regression quantiles with errors-in-variables

In a lot of situations, variables are measured with errors. While this problem has been previously studied in the kontext of kernel regression, no work has been done in quantile regression. To estimate this function we use deconvoluting kernel estimators. The asymptotic behaviour of these estimators depends on the smoothness of the noise distribution.

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Year of publication:	2003
Authors:	Ioannides, D. A. ; Matzner-Lober, E.
Publisher:	Berlin : Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Subject:	Regression \| Schätztheorie \| Theorie

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Series:	SFB 373 Discussion Paper ; 2003,32
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	379165465 [GVK] hdl:10419/22247 [Handle] RePEc:zbw:sfb373:200332 [RePEc]
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010296468