IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2023i6p1676-1691.html
   My bibliography  Save this article

Orthogonality based penalized GMM estimation for variable selection in partially linear spatial autoregressive models

Author

Listed:
  • Peixin Zhao
  • Haogeng Gan
  • Suli Cheng
  • Xiaoshuang Zhou

Abstract

By combining penalized GMM estimation method with the QR decomposition technique, we propose an orthogonal projection-based regularization estimation method for a class of partially linear spatial autoregressive models. The proposed method can select important covariates in the parametric component, and can also identify the significance of spatial effects. Under some conditions, some theoretical properties are studied, such as the consistency of the proposed variable selection procedure and the oracle property of the resulting estimators for parametric and nonparametric components. Furthermore, some simulation studies are carried out to examine the finite sample performances of the proposed regularization estimation method.

Suggested Citation

  • Peixin Zhao & Haogeng Gan & Suli Cheng & Xiaoshuang Zhou, 2023. "Orthogonality based penalized GMM estimation for variable selection in partially linear spatial autoregressive models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(6), pages 1676-1691, March.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:6:p:1676-1691
    DOI: 10.1080/03610926.2021.1937652
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1937652
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1937652?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.

    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:lstaxx:v:52:y:2023:i:6:p:1676-1691. 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/lsta .

    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.