IDEAS home Printed from https://ideas.repec.org/p/ecb/ecbwps/20222676.html
   My bibliography  Save this paper

How well-behaved are revisions to quarterly fiscal data in the euro area?

Author

Listed:
  • Bańkowski, Krzysztof
  • Faria, Thomas
  • Schall, Robert

Abstract

Since most macroeconomic data are revised after the initial release both researchers andpolicy-makers have no choice rather than recognising and understanding the revisions. Thispaper analyses revisions to the fiscal data in the euro area, also by contrasting them with the’better-understood’ macro revisions. Concretely, the study verifies whether fiscal revisionsfulfil requirements to treat them as well-behaved. To this end, we construct a fiscal quarterlyreal-time dataset, which contains quarterly releases of Government Finance Statistics andwhich is supplemented by macro variables from Main National Accounts. Fiscal revisionsdo not satisfy desirable properties expected from well-behaved revisions. In particular, theytend to have a positive bias, they exhibit a big dispersion and they are largely predictable.Also, they are similar to macro revisions, in particular since 2014, which contradicts theoften heard view about fiscal data being subject to particularly large revisions. JEL Classification: C80, E62

Suggested Citation

  • Bańkowski, Krzysztof & Faria, Thomas & Schall, Robert, 2022. "How well-behaved are revisions to quarterly fiscal data in the euro area?," Working Paper Series 2676, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20222676
    Note: 2648110
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2676~65f27f7ac1.en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    2. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michele Modugno, 2012. "An Area-Wide Real-Time Database for the Euro Area," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1000-1013, November.
    3. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    4. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-419, June.
    5. Pedregal, Diego J. & Pérez, Javier J., 2010. "Should quarterly government finance statistics be used for fiscal surveillance in Europe?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 794-807, October.
    6. Francisco Castro & Javier J. P√Ârez & Marta Rodr√Çguez-Vives, 2013. "Fiscal Data Revisions in Europe," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(6), pages 1187-1209, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emilia Tomczyk, 2013. "End of sample vs. real time data: perspectives for analysis of expectations," Working Papers 68, Department of Applied Econometrics, Warsaw School of Economics.
    2. Jacopo Cimadomo, 2016. "Real-Time Data And Fiscal Policy Analysis: A Survey Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 302-326, April.
    3. Marek RUSNAK, 2013. "Revisions to the Czech National Accounts: Properties and Predictability," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(3), pages 244-261, July.
    4. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    5. Dmitry Gornostaev & Alexey Ponomarenko & Sergei Seleznev & Alexandra Sterkhova, 2022. "A Real-Time Historical Database of Macroeconomic Indicators for Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 88-103, March.
    6. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, University of Reading.
    7. Pierre Siklos, 2006. "What Can We Learn from Comprehensive Data Revisions for Forecasting Inflation: Some US Evidence," Working Papers eg0049, Wilfrid Laurier University, Department of Economics, revised 2006.
    8. Martin D. D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    9. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    10. Hughes Hallett, Andrew & Bernoth, Kerstin & Lewis, John, 2008. "Did Fiscal Policy Makers Know What They Were Doing? Reassessing Fiscal Policy with Real Time Data," CEPR Discussion Papers 6758, C.E.P.R. Discussion Papers.
    11. Nicolas Pinkwart, 2011. "Zur Stabilität von Saisonbereinigungsverfahren: Eine Echtzeitdaten-Analyse am Beispiel BV4.1 und X-12-ARIMA," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 5(2), pages 125-144, August.
    12. Bernoth, Kerstin & Hughes Hallett, Andrew & Lewis, John, 2008. "Did Fiscal Policy Makers Know What They Were Doing? Reassessing Fiscal Policy with Real Time Data," CEPR Discussion Papers 6758, C.E.P.R. Discussion Papers.
    13. Clements, Michael P. & Galvao, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation," Economic Research Papers 269743, University of Warwick - Department of Economics.
    14. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2009. "A quarterly fiscal database for the euro area based on intra-annual fiscal information," Working Paper Series 1132, European Central Bank.
    15. Casares, Miguel & Vázquez, Jesús, 2016. "Data Revisions In The Estimation Of Dsge Models," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1683-1716, October.
    16. Tom Bernhardsen & ØYvind Eitrheim, 2005. "Real-time data for Norway: Output gap revisions and challenges for monetary policy," Computing in Economics and Finance 2005 274, Society for Computational Economics.
    17. Bernhardsen, Tom & Eitrheim, Oyvind & Jore, Anne Sofie & Roisland, Oistein, 2005. "Real-time data for Norway: Challenges for monetary policy," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 333-349, December.
    18. Nelson, Edward & Nikolov, Kalin, 2003. "UK inflation in the 1970s and 1980s: the role of output gap mismeasurement," Journal of Economics and Business, Elsevier, vol. 55(4), pages 353-370.
    19. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    20. Nikolsko-Rzhevskyy, Alex, 2008. "Monetary Policy Evaluation in Real Time: Forward-Looking Taylor Rules Without Forward-Looking Data," MPRA Paper 11352, University Library of Munich, Germany.

    More about this item

    Keywords

    data revisions; Fiscal policy; real-time data;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory

    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:ecb:ecbwps:20222676. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.