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Quantile Regression

Roger Koenker and Kevin Hallock

Journal of Economic Perspectives, 2001, vol. 15, issue 4, 143-156

Abstract: Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. The central special case is the median regression estimator which minimizes a sum of absolute errors. Other conditional quantile functions are estimated by minimizing an asymmetrically weighted sum of absolute errors. Quantile regression methods are illustrated with applications to models for CEO pay, food expenditure, and infant birthweight.

JEL-codes: C20 (search for similar items in EconPapers)
Date: 2001
Note: DOI: 10.1257/jep.15.4.143
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http://www.aeaweb.org/articles.php?doi=10.1257/jep.15.4.143 (application/pdf)

Related works:
Book: Quantile Regression (2005)
Book: Quantile Regression (2005)
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