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Quantile regression for binary performance indicators

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  • Paul Hewson
  • Keming Yu

Abstract

Quantile regression is an emerging modelling technique; we examine an approach allowing this technique to model binomial variables in a Bayesian framework and illustrate the value of this advanced technique on a set of local government performance indicators from England and Wales. In U.K. local government, there is currently particular interest in assessing performance relative to ‘top’ and ‘bottom’ quartiles; all authorities are expected to match the current best quartile performance within 5 years, any authority in the ‘bottom’ quartile is assumed to be significantly below par. By its very nature, quantile regression lets us to explore relationships between various covariates and these particular levels of performance. Additionally, by examining a number of other percentiles, we demonstrate how quantile regression gives a much fuller insight into the apparent behaviour of the system we are modelling. Rather than relying on asymptotic results, we use Bayesian methods that allow us to explore the uncertainty implicit in our model building and predictions. We suggest that this is most important when analysing data that are used to make managerial and administrative decisions. Copyright © 2008 John Wiley & Sons, Ltd.

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  • Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    • Handle: RePEc:wly:apsmbi:v:24:y:2008:i:5:p:401-418
    DOI: 10.1002/asmb.732
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