IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v37y2025i6p1605-1623.html
   My bibliography  Save this article

Model Averaging Under Flexible Loss Functions

Author

Listed:
  • Dieqi Gu

    (International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230052, China; and Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon, Hong Kong)

  • Qingfeng Liu

    (Department of Industrial and Systems Engineering, Hosei University, Koganei, Tokyo 184-8584, Japan)

  • Xinyu Zhang

    (International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230052, China; and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100045, China)

Abstract

To address model uncertainty under flexible loss functions in prediction problems, we propose a model averaging method that accommodates various loss functions, including asymmetric linear and quadratic loss functions as well as many other asymmetric/symmetric loss functions as special cases. The flexible loss function allows the proposed method to average a large range of models such as the quantile and expectile regression models. To determine the weights of the candidate models, we establish a J-fold cross-validation criterion. Asymptotic optimality and weight convergence are proved for the proposed method. Simulations and an empirical application show the superior performance of the proposed method compared with other methods of model selection and averaging.

Suggested Citation

  • Dieqi Gu & Qingfeng Liu & Xinyu Zhang, 2025. "Model Averaging Under Flexible Loss Functions," INFORMS Journal on Computing, INFORMS, vol. 37(6), pages 1605-1623, November.
    • Handle: RePEc:inm:orijoc:v:37:y:2025:i:6:p:1605-1623
    DOI: 10.1287/ijoc.2023.0291
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijoc.2023.0291
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ijoc.2023.0291?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
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:inm:orijoc:v:37:y:2025:i:6:p:1605-1623. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.