IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v30y2012i1p41-52.html
   My bibliography  Save this article

One for All and All for One: Regression Checks With Many Regressors

Author

Listed:
  • Pascal Lavergne
  • Valentin Patilea

Abstract

We develop a novel approach to building checks of parametric regression models when many regressors are present, based on a class of sufficiently rich semiparametric alternatives, namely single-index models. We propose an omnibus test based on the kernel method that performs against a sequence of directional nonparametric alternatives as if there was only one regressor whatever the number of regressors. This test can be viewed as a smooth version of the integrated conditional moment test of Bierens. Qualitative information can be easily incorporated into the procedure to enhance power. In an extensive comparative simulation study, we find that our test is not very sensitive to the smoothing parameter and performs well in multidimensional settings. We apply this test to a cross-country growth regression model.

Suggested Citation

  • Pascal Lavergne & Valentin Patilea, 2012. "One for All and All for One: Regression Checks With Many Regressors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 41-52.
  • Handle: RePEc:taf:jnlbes:v:30:y:2012:i:1:p:41-52
    DOI: 10.1198/jbes.2011.07152
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1198/jbes.2011.07152
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1198/jbes.2011.07152?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Miller, Forrest R. & Neill, James W., 2016. "Lack of fit tests for linear regression models with many predictor variables using minimal weighted maximal matchings," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 14-26.
    2. Xie, Chuanlong & Zhu, Lixing, 2019. "A goodness-of-fit test for variable-adjusted models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 27-48.
    3. Li, Lingzhu & Chiu, Sung Nok & Zhu, Lixing, 2019. "Model checking for regressions: An approach bridging between local smoothing and global smoothing methods," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 64-82.
    4. Junmin Liu & Deli Zhu & Luoyao Yu & Xuehu Zhu, 2023. "Specification testing of partially linear single-index models: a groupwise dimension reduction-based adaptive-to-model approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 232-262, March.
    5. Liu, Ran & Zhu, Lixing, 2023. "Specification testing for ordinary differential equation models with fixed design and applications to COVID-19 epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    6. Chuanlong Xie & Lixing Zhu, 2018. "A minimum projected-distance test for parametric single-index Berkson models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 700-715, September.
    7. Jun Lu & Lu Lin, 2020. "Model-free conditional screening via conditional distance correlation," Statistical Papers, Springer, vol. 61(1), pages 225-244, February.
    8. Maistre, Samuel & Lavergne, Pascal & Patilea, Valentin, 2014. "Powerful nonparametric checks for quantile regression," TSE Working Papers 14-501, Toulouse School of Economics (TSE).
    9. Cuizhen Niu & Lixing Zhu, 2018. "A robust adaptive-to-model enhancement test for parametric single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1013-1045, 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:taf:jnlbes:v:30:y:2012:i:1:p:41-52. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UBES20 .

    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.