Quantile Regression

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 es...

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Bibliographic Details
Published in:The Journal of economic perspectives 2001-10, Vol.15 (4), p.143-156
Main Authors: Koenker, Roger, Hallock, Kevin F.
Format: Article
Language:English
Subjects:
Birth weight
Chief executive officers
Data analysis
Econometrics
Economic models
Economic theory
Economics
Empirical research
Estimates
Executive compensation
Infants
Labor economics
Least squares
Linear regression
Pregnancy
Quantile regression
Regression analysis
Secondary schools
Statistical median
Symposium: Econometric Tools
Wages & salaries
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Summary: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.
ISSN:0895-3309
1944-7965
DOI:10.1257/jep.15.4.143