IDEAS home Printed from https://ideas.repec.org/b/cup/cbooks/9780521845731.html
   My bibliography  Save this book

Quantile Regression

Author

Listed:
  • Koenker,Roger

Abstract

Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.

Suggested Citation

  • Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    • Handle: RePEc:cup:cbooks:9780521845731
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
    2. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    3. James M. Poterba & Kim S. Rueben, 1994. "The Distribution of Public Sector Wage Premia: New Evidence Using Quantile Regression Methods," NBER Working Papers 4734, National Bureau of Economic Research, Inc.
    4. Čížek, Pavel, 1999. "Quantile regression," SFB 373 Discussion Papers 1999,78, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    6. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
    7. Amanda Gosling & Stephen Machin & Costas Meghir, 2000. "The Changing Distribution of Male Wages in the U.K," Review of Economic Studies, Oxford University Press, vol. 67(4), pages 635-666.
    8. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    9. Angel López-Nicolás & Jaume García & Pedro J. Hernández, 2001. "How wide is the gap? An investigation of gender wage differences using quantile regression," Empirical Economics, Springer, vol. 26(1), pages 149-167.
    10. Trede, Mark, 1998. "Making mobility visible: a graphical device," Economics Letters, Elsevier, vol. 59(1), pages 77-82, April.
    11. Alberto Abadie & Joshua D. Angrist & Guido W. Imbens, 1998. "Instrumental Variables Estimation of Quantile Treatment Effects," NBER Technical Working Papers 0229, National Bureau of Economic Research, Inc.
    12. José A. F. Machado & José Mata, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465, May.
    13. Kahn, Lawrence M, 1998. "Collective Bargaining and the Interindustry Wage Structure: International Evidence," Economica, London School of Economics and Political Science, vol. 65(260), pages 507-534, November.
    14. Manning, Willard G. & Blumberg, Linda & Moulton, Lawrence H., 1995. "The demand for alcohol: The differential response to price," Journal of Health Economics, Elsevier, vol. 14(2), pages 123-148, June.
    15. Jason Abrevaya, 2001. "The effects of demographics and maternal behavior on the distribution of birth outcomes," Empirical Economics, Springer, vol. 26(1), pages 247-257.
    16. Moshe Buchinsky, 1998. "The dynamics of changes in the female wage distribution in the USA: a quantile regression approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 1-30.
    17. Shih-Kang Chao & Wolfgang Karl Härdle & Weining Wang, 2012. "Quantile Regression in Risk Calibration," SFB 649 Discussion Papers SFB649DP2012-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    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. Stephen Mak & Lennon Choy & Winky Ho, 2010. "Quantile Regression Estimates of Hong Kong Real Estate Prices," Urban Studies, Urban Studies Journal Limited, vol. 47(11), pages 2461-2472, October.
    2. Rahman, Mustafizur & Al-Hasan, Md., 2018. "Male-Female wage gap and informal employment in Bangladesh: A quantile regression approach," MPRA Paper 90131, University Library of Munich, Germany.
    3. Jean-Louis Arcand & Béatrice D'hombres, 2004. "Racial discrimination in the Brazilian labour market: wage, employment and segregation effects," Journal of International Development, John Wiley & Sons, Ltd., vol. 16(8), pages 1053-1066.
    4. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    5. RAMDANI, Dendi & VAN WITTELOOSTUIJN, Arjen, 2009. "Board independence, CEO duality and firm performance: A quantile regression analysis for Indonesia, Malaysia, South Korea and Thailand," ACED Working Papers 2009003, University of Antwerp, Faculty of Business and Economics.
    6. Chris Herbst & Erdal Tekin, 2011. "Child care subsidies and childhood obesity," Review of Economics of the Household, Springer, vol. 9(3), pages 349-378, September.
    7. Thomas Grandner & Dieter Gstach, 2012. "Decomposing wage discrimination in Germany and Austria with counterfactual densities," Department of Economics Working Papers wuwp145, Vienna University of Economics and Business, Department of Economics.
    8. Anh Tram Le & Paul W. Miller & Barry R. Chiswick, 2006. "The Immigrant-Native Born Earnings Gap in the US: a Quantile Regression Analysis and International Comparison," Economics Discussion / Working Papers 06-04, The University of Western Australia, Department of Economics.
    9. McMillen, Daniel P., 2008. "Changes in the distribution of house prices over time: Structural characteristics, neighborhood, or coefficients?," Journal of Urban Economics, Elsevier, vol. 64(3), pages 573-589, November.
    10. RAMDANI, Dendi & VAN WITTELOOSTUIJN, Arjen, 2009. "Board independence, CEO duality and firm performance: A quantile regression analysis for Indonesia, Malaysia, South Korea and Thailand," Working Papers 2009004, University of Antwerp, Faculty of Business and Economics.
    11. Monserrat Bustelo, 2004. "Caracterización de los Cambios en la Desigualdad y la Pobreza en Argentina Haciendo Uso de Técnicas de Descomposiciones Microeconometricas (1992-2001)," CEDLAS, Working Papers 0013, CEDLAS, Universidad Nacional de La Plata.
    12. Chiswick, Barry R. & Le, Anh T. & Miller, Paul W., 2006. "How Immigrants Fare Across the Earnings Distribution: International Analyses," IZA Discussion Papers 2405, Institute of Labor Economics (IZA).
    13. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    14. Arias, Omar & Yamada, Gustavo & Tejerina, Luis, 2018. "Education, Family Background and Racial Earnings Inequality in Brazil," IDB Publications (Working Papers) 4369, Inter-American Development Bank.
    15. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    16. Costa-Font, Joan & Fabbri, Daniele & Gil, Joan, 2009. "Decomposing body mass index gaps between Mediterranean countries: A counterfactual quantile regression analysis," Economics & Human Biology, Elsevier, vol. 7(3), pages 351-365, December.
    17. Mona Said & Fatma El-Hamidi, 2008. "Taking Technical Education Seriously in MENA: Determinants, Labor Market Implications and Policy Lessons," Working Papers 450, Economic Research Forum, revised 09 Jan 2008.
    18. Joanna Małgorzata Landmesser, 2019. "Decomposition Of Gender Wage Gap In Poland Using Counterfactual Distribution With Sample Selection," Statistics in Transition New Series, Polish Statistical Association, vol. 20(3), pages 171-186, September.
    19. Töpfer, Marina, 2017. "Detailed RIF Decomposition with Selection - The Gender Pay Gap in Italy," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168422, Verein für Socialpolitik / German Economic Association.
    20. Christopher Bollinger & James P. Ziliak & Kenneth R. Troske, 2011. "Down from the Mountain: Skill Upgrading and Wages in Appalachia," Journal of Labor Economics, University of Chicago Press, vol. 29(4), pages 819-857.

    More about this item

    JEL classification:

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

    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:cup:cbooks:9780521845731. 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://www.cambridge.org .

    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: Ruth Austin The email address of this maintainer does not seem to be valid anymore. Please ask Ruth Austin to update the entry or send us the correct address (email available below). General contact details of provider: https://www.cambridge.org .

    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.