IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v40y2024i2p777-795.html
   My bibliography  Save this article

Financial-cycle ratios and medium-term predictions of GDP: Evidence from the United States

Author

Listed:
  • Moramarco, Graziano

Abstract

Using a large quarterly macroeconomic dataset for the period 1960–2017, we document the ability of specific financial ratios from the housing market and firms’ aggregate balance sheets to predict GDP over medium-term horizons in the United States. A cyclically adjusted house price-to-rent ratio and the liabilities-to-income ratio of the non-financial non-corporate business sector provide the best in-sample and out-of-sample predictions of GDP growth over horizons of one to five years, based on a wide variety of rankings. Small forecasting models that include these indicators outperform popular high-dimensional models and forecast combinations. The predictive power of the two ratios appears strong during both recessions and expansions, stable over time, and consistent with well-established macro-finance theory.

Suggested Citation

  • Moramarco, Graziano, 2024. "Financial-cycle ratios and medium-term predictions of GDP: Evidence from the United States," International Journal of Forecasting, Elsevier, vol. 40(2), pages 777-795.
    • Handle: RePEc:eee:intfor:v:40:y:2024:i:2:p:777-795
    DOI: 10.1016/j.ijforecast.2023.05.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207023000523
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2023.05.007?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:2:p:777-795. 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.