IDEAS home Printed from https://ideas.repec.org/a/cup/etheor/v2y1986i02p191-201_01.html
   My bibliography  Save this article

Strong Consistency of Regression Quantiles and Related Empirical Processes

Author

Listed:
  • Bassett, Gilbert W.
  • Koenker, Roger W.

Abstract

The strong consistency of regression quantile statistics (Koenker and Bassett [4]) in linear models with iid errors is established. Mild regularity conditions on the regression design sequence and the error distribution are required. Strong consistency of the associated empirical quantile process (introduced in Bassett and Koenker [1]) is also established under analogous conditions. However, for the proposed estimate of the conditional distribution function of Y, no regularity conditions on the error distribution are required for uniform strong convergence, thus establishing a Glivenko-Cantelli-type theorem for this estimator.

Suggested Citation

  • Bassett, Gilbert W. & Koenker, Roger W., 1986. "Strong Consistency of Regression Quantiles and Related Empirical Processes," Econometric Theory, Cambridge University Press, vol. 2(02), pages 191-201, August.
  • Handle: RePEc:cup:etheor:v:2:y:1986:i:02:p:191-201_01
    as

    Download full text from publisher

    File URL: http://journals.cambridge.org/abstract_S0266466600011488
    File Function: link to article abstract page
    Download Restriction: no

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paolo Naticchioni & Andrea Ricci & Emiliano Rustichelli, 2007. "Wage Structure, Inequality And Skill-Biased Change: Is Italy An Outlier?," Quaderni del Dipartimento di Economia, Finanza e Statistica 38/2007, Università di Perugia, Dipartimento Economia.
    2. Mohamed El Ghourabi & Christian Francq & Fedya Telmoudi, 2016. "Consistent Estimation of the Value at Risk When the Error Distribution of the Volatility Model is Misspecified," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 46-76, January.
    3. José Ferreira Machado & Pedro Portugal & Juliana Guimarães, 2006. "U.S. Unemployment Duration: Has Long Become Longer or Short Become Shorter?," Working Papers w200613, Banco de Portugal, Economics and Research Department.
    4. Karun Adusumilli & Taisuke Otsu & Yoon-Jae Whang, 2017. "Inference on distribution functions under measurement error," STICERD - Econometrics Paper Series 594, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. Jungsik Noh & Sangyeol Lee, 2016. "Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 700-720, September.
    6. Juliana Guimarães & (Universidade NOVA de Lisboa, 2004. "Has long become longer or short become shorter? Evidence from a censored quantile regression analysis of the changes in the distribution of U.S. unemployment duration," Econometric Society 2004 Latin American Meetings 128, Econometric Society.
    7. Juan Manuel del Pozo Segura, 2017. " Has the Gender Wage Gap been Reduced during the 'Peruvian Growth Miracle?' A Distributional Approach," Documentos de Trabajo / Working Papers 2017-442, Departamento de Economía - Pontificia Universidad Católica del Perú.
    8. Héctor Ricardo Gertel & Roberto Giuliodori & María Luz Vera & Guadalupe Bastos & Sonia Costanzo, 2010. "Heterogeneidad en el desempeño académico de los estudiantes de Argentina: evidencia a partir de regresión por cuantiles," Investigaciones de Economía de la Educación volume 5,in: María Jesús Mancebón-Torrubia & Domingo P. Ximénez-de-Embún & José María Gómez-Sancho & Gregorio Gim (ed.), Investigaciones de Economía de la Educación 5, edition 1, volume 5, chapter 6, pages 117-138 Asociación de Economía de la Educación.
    9. Yu, Chi Wai & Clarke, Bertrand, 2010. "Asymptotics of Bayesian median loss estimation," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 1950-1958, October.

    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:cup:etheor:v:2:y:1986:i:02:p:191-201_01. 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: (Keith Waters). General contact details of provider: http://journals.cambridge.org/jid_ECT .

    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.

    We have no references for this item. You can help adding them by using 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.

    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.