IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v41y2025i3p1055-1072.html
   My bibliography  Save this article

Econometric forecasting using ubiquitous news text: Text-enhanced factor model

Author

Listed:
  • Seo, Beomseok

Abstract

News text is gaining increasing attention as a novel source for econometric forecasting. This paper revisits how narrative information is incorporated into econometric forecasting by effectively quantifying sector-specific textual information without requiring training data. We propose Theme Frequency Indices (TFIs), which utilize domain-specific subject-predicate patterns to measure public perception about the economy. TFIs for 15 sectors, including production, inflation, employment, capital investment, stock and house prices, and others, were examined and integrated into the Text-enhanced Factor Model (TFM), using latent factor structures. Empirical analysis based on over 18 million news articles from Korea reveals that TFM improves the accuracy of near-term GDP forecasts, demonstrating that simple text-mining techniques combined with domain knowledge can effectively leverage qualitative information in the news without costly training. The proposed method is applicable to a wide range of subjects for utilizing narrative information on the economy, offering a rapid and cost-effective approach.

Suggested Citation

  • Seo, Beomseok, 2025. "Econometric forecasting using ubiquitous news text: Text-enhanced factor model," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1055-1072.
    • Handle: RePEc:eee:intfor:v:41:y:2025:i:3:p:1055-1072
    DOI: 10.1016/j.ijforecast.2024.11.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207024001055
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2024.11.001?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:41:y:2025:i:3:p:1055-1072. 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.