An algorithm for censored quantile regressions

In this paper, we present an algorithm for Censored Quantile Regression (CQR) estimation problems. Our method permits CQR estimation problems to be solved more efficiently and reliably than was hitherto possible. It guarantees to find a high quality estimator in O(k×nÂ²) operations with k regressors and n observations, which is much less than the existing algorithms for CQR problems.

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Year of publication:	2007
Authors:	Stengos, Thanasis ; Wang, Dianqin
Published in:	Economics Bulletin. - AccessEcon, ISSN 1545-2921. - Vol. 3.2007, 1, p. 1-9
Publisher:	AccessEcon
Subject:	Cencored Quantile Regression

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

Persistent link: https://www.econbiz.de/10005110635