IDEAS home Printed from https://ideas.repec.org/a/bla/scotjp/v48y2001i2p179-195.html

Predicting UK Business Cycle Regimes

Author

Listed:
  • Chris Birchenhall
  • Denise Osborn
  • Marianne Sensier

Abstract

This paper uses logistic regression to construct a one‐quarter ahead prediction model for classical business cycle regimes in the UK. The binary dependent variable is obtained by applying simple mechanical rules to date turning points in quarterly real GDP data from 1963 to 1999. Using a range of real and financial leading indicators, several parsimonious one‐quarter‐ahead models are developed for the GDP regimes, with model selection based on the SIC criterion. A real M4 variable is consistently found to have predictive content. One model that performs well combines this with nominal UK and German short‐term interest rates. The role of the latter emphasises the open nature of the UK economy.

Suggested Citation

  • Chris Birchenhall & Denise Osborn & Marianne Sensier, 2001. "Predicting UK Business Cycle Regimes," Scottish Journal of Political Economy, Scottish Economic Society, vol. 48(2), pages 179-195, May.
    • Handle: RePEc:bla:scotjp:v:48:y:2001:i:2:p:179-195
    DOI: 10.1111/1467-9485.00193
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9485.00193
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9485.00193?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
    ---><---

    Other versions of this item:

    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:bla:scotjp:v:48:y:2001:i:2:p:179-195. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/sesssea.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.