IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v24y2008i5p401-418.html
   My bibliography  Save this article

Quantile regression for binary performance indicators

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.732
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.732?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    2. David B. Dunson & M. Watson & Jack A. Taylor, 2003. "Bayesian Latent Variable Models for Median Regression on Multiple Outcomes," Biometrics, The International Biometric Society, vol. 59(2), pages 296-304, June.
    3. Lee, Myoung-jae, 1992. "Median regression for ordered discrete response," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 59-77.
    4. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, November.
    5. Winkelmann, Rainer, 2006. "Reforming health care: Evidence from quantile regressions for counts," Journal of Health Economics, Elsevier, vol. 25(1), pages 131-145, January.
    6. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    7. M. Stone, 2002. "How not to measure the efficiency of public services (and how one might)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(3), pages 405-434, October.
    8. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    9. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    10. David Draper & Mark Gittoes, 2004. "Statistical analysis of performance indicators in UK higher education," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 449-474, August.
    11. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    12. Trevor C. Bailey & Paul J. Hewson, 2004. "Simultaneous modelling of multiple traffic safety performance indicators by using a multivariate generalized linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 501-517, August.
    13. Hauck, Katharina & Street, Andrew, 2006. "Performance assessment in the context of multiple objectives: A multivariate multilevel analysis," Journal of Health Economics, Elsevier, vol. 25(6), pages 1029-1048, November.
    14. Susanne M. Schennach, 2005. "Bayesian exponentially tilted empirical likelihood," Biometrika, Biometrika Trust, vol. 92(1), pages 31-46, March.
    15. Harvey Goldstein & David J. Spiegelhalter, 1996. "League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 385-409, May.
    16. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    17. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
    18. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(2), pages 186-199, June.
    19. Peter Smith, 1990. "The Use of Performance Indicators in the Public Sector," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(1), pages 53-72, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luke B. Smith & Brian J. Reich & Amy H. Herring & Peter H. Langlois & Montserrat Fuentes, 2015. "Multilevel quantile function modeling with application to birth outcomes," Biometrics, The International Biometric Society, vol. 71(2), pages 508-519, June.
    2. Lane F. Burgette & Jerome P. Reiter, 2012. "Modeling Adverse Birth Outcomes via Confirmatory Factor Quantile Regression," Biometrics, The International Biometric Society, vol. 68(1), pages 92-100, March.
    3. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, New Economic School (NES).
    4. Fuzi, Mohd Fadzli Mohd & Jemain, Abdul Aziz & Ismail, Noriszura, 2016. "Bayesian quantile regression model for claim count data," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 124-137.
    5. Chunping Liu & Audrey Laporte & Brian S. Ferguson, 2008. "The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1073-1087, September.
    6. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    7. Alhamzawi, Rahim & Yu, Keming, 2013. "Conjugate priors and variable selection for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 209-219.
    8. Gregg, Daniel & Rolfe, John, 2016. "The value of environment across efficiency quantiles: A conditional regression quantiles analysis of rangelands beef production in north Eastern Australia," Ecological Economics, Elsevier, vol. 128(C), pages 44-54.
    9. Mahmut Yaşar & Catherine J. Morrison Paul, 2009. "Size and Foreign Ownership Effects on Productivity and Efficiency: An Analysis of Turkish Motor Vehicle and Parts Plants," Review of Development Economics, Wiley Blackwell, vol. 13(4), pages 576-591, November.
    10. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    11. Cariou, Pierre & Wolff, Francois-Charles, 2015. "Identifying substandard vessels through Port State Control inspections: A new methodology for Concentrated Inspection Campaigns," Marine Policy, Elsevier, vol. 60(C), pages 27-39.
    12. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    13. Nicholas Longford & D. B. Rubin, 2006. "Performance assessment and league tables. Comparing like with like," Economics Working Papers 994, Department of Economics and Business, Universitat Pompeu Fabra.
    14. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly & Kaspar Wüthrich, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 123-137, January.
    15. Nils Gutacker & Andrew Street, 2015. "Multidimensional performance assessment using dominance criteria," Working Papers 115cherp, Centre for Health Economics, University of York.
    16. Holger Dette & Marc Hallin & Tobias Kley & Stanislav Volgushev, 2011. "Of Copulas, Quantiles, Ranks and Spectra - An L1-Approach to Spectral Analysis," Working Papers ECARES ECARES 2011-038, ULB -- Universite Libre de Bruxelles.
    17. Salaheddine El Adlouni, 2018. "Quantile regression C-vine copula model for spatial extremes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(1), pages 299-317, October.
    18. Guodong Li & Yang Li & Chih-Ling Tsai, 2015. "Quantile Correlations and Quantile Autoregressive Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 246-261, March.
    19. Yukiko Omata & Hajime Katayama & Toshi. H. Arimura, 2017. "Same concerns, same responses? A Bayesian quantile regression analysis of the determinants for supporting nuclear power generation in Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(3), pages 581-608, July.
    20. Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2019. "Model averaging estimators for the stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 51(2), pages 91-103, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:apsmbi:v:24:y:2008:i:5:p:401-418. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.