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Introduction

  • Bernd Fitzenberger
  • Roger Koenker
  • José A. F. Machado
Chapter
Part of the Studies in Empirical Economics book series (STUDEMP)

Abstract

In the classical methodology of least-squares regression the conditional mean function, the function that describes how the mean of y changes with the vector of covariates x, is (almost) all we need to know about the relationship between y and x. This is often perceived as the ‘systematic component’ around which y fluctuates due to an “erratic component”. The crucial, and convenient, thing about this view is that the error is assumed to have precisely the same distribution whatever values may be taken by the components of the vector x. If this is the case, we can be fully satisfied with an estimated model of the conditional mean function, supplemented perhaps by an estimate of the conditional dispersion of y around its mean.

Keywords

Quantile Regression Wage Inequality Wage Distribution High Quantile High Education Group 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Reference

  1. Koenker, R. and G. Bassett (1978) “Regression Quantiles”, Econometrica, 46, 33–50.CrossRefGoogle Scholar
  2. See also Koenker, R. and K.F. Hallock (2001) “Quantile Regression: An Introduction”, Journal of Economic Perspectives (forthcoming).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Bernd Fitzenberger
  • Roger Koenker
  • José A. F. Machado

There are no affiliations available

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