IDEAS home Printed from https://ideas.repec.org/p/euf/ecopap/0538.html
   My bibliography  Save this paper

The role of survey data in nowcasting euro area GDP growth

Author

Listed:
  • Alessandro Girardi
  • Andreas Reuter
  • Christian Gayer

Abstract

This paper evaluates the impact of new releases of financial, real activity and survey data on the nowcasting of euro area GDP growth. We show that financial data are only essential for improving nowcasting in the first two months of a nowcast quarter. This contrasts with survey and real data, which are indispensable components throughout the entire nowcasting exercise. When treating variables as if they were all published at the same time and without any time lag, financial series lose all their significance, while survey data remain important. This evidence suggests that survey data offer more than just timeliness for the purpose of nowcasting GDP growth. The latter holds true for financial data only when restricting the analysis to the 2008-09 financial crisis.

Suggested Citation

  • Alessandro Girardi & Andreas Reuter & Christian Gayer, 2014. "The role of survey data in nowcasting euro area GDP growth," European Economy - Economic Papers 2008 - 2015 538, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    • Handle: RePEc:euf:ecopap:0538
    as

    Download full text from publisher

    File URL: https://ec.europa.eu/economy_finance/publications/economic_paper/2014/ecp538_en.htm
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    2. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(54), pages 5802-5816.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2021. "Frequency domain analysis and filtering of business and consumer survey expectations," International Economics, CEPII research center, issue 166, pages 42-57.
    4. Evžen Kočenda & Karen Poghosyan, 2020. "Nowcasting Real GDP Growth: Comparison between Old and New EU Countries," Eastern European Economics, Taylor & Francis Journals, vol. 58(3), pages 197-220, May.
    5. Siliverstovs, Boriss, 2017. "Dissecting models' forecasting performance," Economic Modelling, Elsevier, vol. 67(C), pages 294-299.
    6. Simón Sosvilla-Rivero & María del Carmen Ramos-Herrera, 2018. "Inflation, real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters," Applied Economics, Taylor & Francis Journals, vol. 50(42), pages 4540-4555, September.
    7. Gabe J. Bondt, 2019. "A PMI-Based Real GDP Tracker for the Euro Area," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(2), pages 147-170, December.
    8. Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022. "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers 964, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Sergey V. Arzhenovskiy, 2024. "Forecasting GDP Dynamics Based on the Bank of Russia’s Enterprise Monitoring Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 31-44, February.
    10. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    11. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    12. Мекенбаева Камила // Mekenbayeva Kamila & Karel Musil, 2017. "Система прогнозирования в Национальном Банке Казахстана: наукаст на основа опросов // Forecasting system at the National Bank of Kazakhstan: survey-based nowcasting," Working Papers #2017-1, National Bank of Kazakhstan.
    13. Werner Hölzl & Gerhard Schwarz, 2014. "Der WIFO-Konjunkturtest: Methodik und Prognoseeigenschaften," WIFO Monatsberichte (monthly reports), WIFO, vol. 87(12), pages 835-850, December.
    14. Maria Rita Ippoliti & Fabiana Sartor & Luigi Martone, 2021. "Trade surveys: qualitative and quantitative indicators," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(4), pages 75-85, October-D.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:euf:ecopap:0538. 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: ECFIN INFO (email available below). General contact details of provider: https://edirc.repec.org/data/dg2ecbe.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.