IDEAS home Printed from https://ideas.repec.org/a/ecb/ecbbox/201900054.html
   My bibliography  Save this article

Sources of economic policy uncertainty in the euro area: a machine learning approach

Author

Listed:
  • Azqueta-Gavaldon, Andres
  • Hirschbühl, Dominik
  • Onorante, Luca
  • Saiz, Lorena

Abstract

This box presents a model-based economic policy uncertainty (EPU) index for the euro area by applying machine learning techniques to news articles from January 2000 to May 2019. The machine learning algorithm retrieves components of overall EPU, such as trade, fiscal, monetary or domestic regulations, for a wide range of languages. Recently, a steady and pronounced increase in the euro area EPU index has been observed, driven mainly by trade, domestic regulation and fiscal policy uncertainties. JEL Classification: C1, C8, E65

Suggested Citation

  • Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2019. "Sources of economic policy uncertainty in the euro area: a machine learning approach," Economic Bulletin Boxes, European Central Bank, vol. 5.
  • Handle: RePEc:ecb:ecbbox:2019:0005:4
    Note: 2460732
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/economic-bulletin/focus/2019/html/ecb.ebbox201905_04~6b149ccb66.en.html
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Liosi, Konstantina, 2023. "The sources of economic uncertainty: Evidence from eurozone markets," Journal of Multinational Financial Management, Elsevier, vol. 69(C).
    3. Baumgärtner, Martin & Zahner, Johannes, 2023. "Whatever it takes to understand a central banker: Embedding their words using neural networks," IMFS Working Paper Series 194, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    4. Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.

    More about this item

    Keywords

    Economic Policy Uncertainty; machine learning; sources of uncertainty; text-mining;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E65 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Studies of Particular Policy Episodes

    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:ecb:ecbbox:2019:0005:4. 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: Official Publications (email available below). General contact details of provider: https://edirc.repec.org/data/emieude.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.