IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v40y2024i1p202-228.html
   My bibliography  Save this article

Equal predictive ability tests based on panel data with applications to OECD and IMF forecasts

Author

Listed:
  • Akgun, Oguzhan
  • Pirotte, Alain
  • Urga, Giovanni
  • Yang, Zhenlin

Abstract

We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, S-statistics, focuses on the overall EPA hypothesis, which states that the EPA holds, on average, over all panel units and over time. The second type, C-statistics, focuses on the clustered EPA hypothesis where the EPA holds jointly for a fixed number of clusters of panel units. The asymptotic properties of the proposed tests are evaluated under weak and strong cross-sectional dependence. An extensive Monte Carlo simulation shows that the proposed tests have very good finite sample properties, even with little information about the cross-sectional dependence in the data. The proposed framework is applied to compare the economic growth forecasts of the OECD and the IMF, and to evaluate the performance of the consumer price inflation forecasts of the IMF.

Suggested Citation

  • Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni & Yang, Zhenlin, 2024. "Equal predictive ability tests based on panel data with applications to OECD and IMF forecasts," International Journal of Forecasting, Elsevier, vol. 40(1), pages 202-228.
    • Handle: RePEc:eee:intfor:v:40:y:2024:i:1:p:202-228
    DOI: 10.1016/j.ijforecast.2023.02.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207023000092
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2023.02.001?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.

    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:eee:intfor:v:40:y:2024:i:1:p:202-228. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.