IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v81y2019i3p629-648.html
   My bibliography  Save this article

Lack‐of‐fit tests for quantile regression models

Author

Listed:
  • Chen Dong
  • Guodong Li
  • Xingdong Feng

Abstract

The paper novelly transforms lack‐of‐fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two‐sample test statistics in the literature, two tests are constructed to check the lack of fit for low and high dimensional quantile regression models. The low dimensional test works well when the number of covariates is moderate, whereas the high dimensional test can maintain the power when the number of covariates exceeds the sample size. The null distribution of the high dimensional test has an explicit form, and the p‐values or critical values can then be calculated directly. The finite sample performance of the tests proposed is examined by simulation studies, and their usefulness is further illustrated by two real examples.

Suggested Citation

  • Chen Dong & Guodong Li & Xingdong Feng, 2019. "Lack‐of‐fit tests for quantile regression models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(3), pages 629-648, July.
    • Handle: RePEc:bla:jorssb:v:81:y:2019:i:3:p:629-648
    DOI: 10.1111/rssb.12321
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssb.12321
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssb.12321?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
    ---><---

    Citations

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


    Cited by:

    1. Ana Pérez-González & Tomás R. Cotos-Yáñez & Wenceslao González-Manteiga & Rosa M. Crujeiras-Casais, 2021. "Goodness-of-fit tests for quantile regression with missing responses," Statistical Papers, Springer, vol. 62(3), pages 1231-1264, June.
    2. Feng, Xingdong & Liu, Qiaochu & Wang, Caixing, 2023. "A lack-of-fit test for quantile regression process models," Statistics & Probability Letters, Elsevier, vol. 192(C).
    3. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "A Nonparametric Test for Testing Heterogeneity in Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202117, University of Kansas, Department of Economics, revised Aug 2021.
    4. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.

    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:bla:jorssb:v:81:y:2019:i:3:p:629-648. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    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://edirc.repec.org/data/rssssea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.