IDEAS home Printed from https://ideas.repec.org/a/nsr/niesra/i14y2024p8-11.html
   My bibliography  Save this article

Box A: A Neural Network Approach to Forecasting Inflation

Author

Listed:
  • Jane Binner
  • Huw Dixon
  • Barry Jones
  • Jon Tepper

Abstract

We construct the following two types of MRN models of inflation: a) a simple MRN consisting of two input variables: the mom percentage change in inflation (auto-regressive term) and the natural log of the price level; b)a complex MRN that includes the following additional five exogenous variables: real GDP, PPI index for industrial output, UK gilts 10 year yield, sterling effective exchange rate (index level) and adjusted Divisia price dual. (See Binner et al. (2023) for details of the construction of the Divisia price dual).

Suggested Citation

  • Jane Binner & Huw Dixon & Barry Jones & Jon Tepper, 2024. "Box A: A Neural Network Approach to Forecasting Inflation," National Institute UK Economic Outlook, National Institute of Economic and Social Research, issue 14, pages 8-11.
    • Handle: RePEc:nsr:niesra:i:14:y:2024:p:8-11
    as

    Download full text from publisher

    File URL: https://www.niesr.ac.uk/wp-content/uploads/2024/05/UK-Economic-Outlook-Spring-2024.pdf?ver=cCJd8w3xFqJgLcijdhTg
    Download Restriction: no
    ---><---

    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:nsr:niesra:i:14:y:2024:p:8-11. 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: Library & Information Manager (email available below). General contact details of provider: https://edirc.repec.org/data/niesruk.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.