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Quantile Regression: An Overview

In: Quantile Regression for Spatial Data

Author

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  • Daniel P. McMillen

    (University of Illinois)

Abstract

Linear regression is the standard tool for empirical studies in most of the social sciences. When the relationship between a dependent variable, y, and a set of explanatory variables, X, can be written as $$ y = X\beta + u $$ , a simple ordinary least squares (OLS) regression of y on X can potentially provide unbiased estimates of the parameters, β, and a predicted value, $$ \widehat{y} = X\widehat{\beta } $$ is the best guess of the value of y given values for X. A glance at any journal in the social sciences quickly reveals the dominance of regression analysis as the tool for empirical analysis.

Suggested Citation

  • Daniel P. McMillen, 2013. "Quantile Regression: An Overview," SpringerBriefs in Regional Science, in: Quantile Regression for Spatial Data, edition 127, chapter 0, pages 1-11, Springer.
    • Handle: RePEc:spr:sbrchp:978-3-642-31815-3_1
    DOI: 10.1007/978-3-642-31815-3_1
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