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Quantile Regression

Author

Listed:
  • 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.

Suggested Citation

  • Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    • 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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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jep.15.4.143
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    References listed on IDEAS

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    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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