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Quantile Regression

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  • Roger Koenker
  • Kevin F. Hallock

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.

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Bibliographic Info

Article provided by American Economic Association in its journal Journal of Economic Perspectives.

Volume (Year): 15 (2001)
Issue (Month): 4 (Fall)
Pages: 143-156

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Handle: RePEc:aea:jecper:v:15:y:2001:i:4:p:143-156

Note: DOI: 10.1257/jep.15.4.143
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  9. Gosling, Amanda & Machin, Stephen & Meghir, Costas, 2000. "The Changing Distribution of Male Wages in the U.K," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 67(4), pages 635-66, October.
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