IDEAS home Printed from https://ideas.repec.org/a/eee/jmacro/v85y2025ics0164070425000278.html
   My bibliography  Save this article

Learning from news

Author

Listed:
  • Herrera, Luis
  • Vázquez, Jesús

Abstract

This paper contributes to two strands of business cycle literature — news shocks and bounded rationality — by assessing the empirical importance of TFP news shocks while relaxing the rational expectations assumption. We estimate a medium-scale DSGE model, incorporating financial frictions and TFP news shocks, under two different expectation formation mechanisms: rational expectations (RE) and adaptive learning (AL). The results suggest that AL amplifies the effects of financial market frictions, leading to three key findings. First, AL improves the model’s fit, as shown in the related literature, and better replicates the volatility of several aggregate variables. Second, the AL amplification results in a deflationary response and a more persistent reaction of lending spreads to TFP news shocks. Third, AL increases the importance of pure news shocks (i.e. purely anticipated shocks), amplifying their effects through both expectation and credit channels. Finally, we show that the dynamics generated by the DSGE model under AL align more closely with empirical VAR evidence than those produced by the RE version of the DSGE model.

Suggested Citation

  • Herrera, Luis & Vázquez, Jesús, 2025. "Learning from news," Journal of Macroeconomics, Elsevier, vol. 85(C).
  • Handle: RePEc:eee:jmacro:v:85:y:2025:i:c:s0164070425000278
    DOI: 10.1016/j.jmacro.2025.103690
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0164070425000278
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jmacro.2025.103690?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;

    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:eee:jmacro:v:85:y:2025:i:c:s0164070425000278. 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/inca/622617 .

    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.