IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v41y2022i7p775-805.html
   My bibliography  Save this article

Optimal model averaging for divergent-dimensional Poisson regressions

Author

Listed:
  • Jiahui Zou
  • Wendun Wang
  • Xinyu Zhang
  • Guohua Zou

Abstract

This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback–Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.

Suggested Citation

  • Jiahui Zou & Wendun Wang & Xinyu Zhang & Guohua Zou, 2022. "Optimal model averaging for divergent-dimensional Poisson regressions," Econometric Reviews, Taylor & Francis Journals, vol. 41(7), pages 775-805, August.
    • Handle: RePEc:taf:emetrv:v:41:y:2022:i:7:p:775-805
    DOI: 10.1080/07474938.2022.2047508
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07474938.2022.2047508
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07474938.2022.2047508?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. Walena Anesu Marambakuyana & Sandile Charles Shongwe, 2024. "Composite and Mixture Distributions for Heavy-Tailed Data—An Application to Insurance Claims," Mathematics, MDPI, vol. 12(2), pages 1-23, January.

    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:emetrv:v:41:y:2022:i:7:p:775-805. 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.