IDEAS home Printed from https://ideas.repec.org/a/eee/eecrev/v172y2025ics0014292124002770.html
   My bibliography  Save this article

Learning to be rational in the presence of news: A lab investigation

Author

Listed:
  • Lustenhouwer, Joep
  • Salle, Isabelle

Abstract

We conduct a laboratory experiment in a micro-founded macroeconomic model where participants receive public announcements about future government spending shocks, and are tasked with repeatedly forecasting output over a given horizon. By eliciting several-period-ahead predictions, we can investigate forecast revisions in relation to these announcements. We find that subjects learn the magnitude of the effect of the shocks on output, albeit not with perfect accuracy. We find micro-level evidence that they persistently underreact to the announcements in a way consistent with sticky information, but find little support for fully backward-looking expectations. We rationalize the experimental data with a Bayesian updating model, which provides a particularly good description of the behaviors in longer-horizon environments and among attentive, experienced, and effortful subjects.

Suggested Citation

  • Lustenhouwer, Joep & Salle, Isabelle, 2025. "Learning to be rational in the presence of news: A lab investigation," European Economic Review, Elsevier, vol. 172(C).
  • Handle: RePEc:eee:eecrev:v:172:y:2025:i:c:s0014292124002770
    DOI: 10.1016/j.euroecorev.2024.104948
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0014292124002770
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.euroecorev.2024.104948?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:eecrev:v:172:y:2025:i:c:s0014292124002770. 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/eer .

    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.