IDEAS home Printed from https://ideas.repec.org/a/oup/emjrnl/v27y2024i1p151-170..html
   My bibliography  Save this article

Revealing priors from posteriors with an application to inflation forecasting in the UK

Author

Listed:
  • Masako Ikefuji
  • Jan R Magnus
  • Takashi Yamagata

Abstract

SummaryA Bayesian typically uses data and a prior to produce a posterior. We shall follow the opposite route, using data and the posterior information to reveal the prior. We then apply this theory to inflation forecasts by the Bank of England and the National Institute of Economic and Social Research in an attempt to get some insight into the prior beliefs of the policy makers in these two institutions, especially under the uncertainties about the Brexit referendum, the Covid-19 lockdown, and the Russian invasion of Ukraine.

Suggested Citation

  • Masako Ikefuji & Jan R Magnus & Takashi Yamagata, 2024. "Revealing priors from posteriors with an application to inflation forecasting in the UK," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 151-170.
    • Handle: RePEc:oup:emjrnl:v:27:y:2024:i:1:p:151-170.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ectj/utad021
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:oup:emjrnl:v:27:y:2024:i:1:p:151-170.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/resssea.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.