IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/98952.html
   My bibliography  Save this paper

Polish GDP Forecast Errors: A Tale of Ineffectiveness

Author

Listed:
  • Rybacki, Jakub

Abstract

The aim of this paper is to evaluate gross domestic product (GDP) forecast errors of Polish professional forecasters based on the individual data from the Rzeczpospolita daily newspaper. This dataset contains predictions on forecasting competitions during the years 2013–2019 in Poland. Our analysis shows a lack of statistical effectiveness of these predictions. First, there is a systemic negative bias, which is especially strong during the years of conservative PiS government rule. Second, the forecasters failed to correctly predict the effects of major changes in fiscal policy. Third, there is evidence of strategic behaviors; for example, the forecasters tended to revise their prognosis too frequently and too excessively. We also document herding behavior, i.e., an alignment of the most extreme forecasts towards market consensus with time, and an overly strong reliance on forecasts from NBP inflation projections in cases of estimates for longer horizons.

Suggested Citation

  • Rybacki, Jakub, 2020. "Polish GDP Forecast Errors: A Tale of Ineffectiveness," MPRA Paper 98952, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:98952
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/98952/1/MPRA_paper_98952.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pina, Álvaro M. & Venes, Nuno M., 2011. "The political economy of EDP fiscal forecasts: An empirical assessment," European Journal of Political Economy, Elsevier, vol. 27(3), pages 534-546, September.
    2. Dovern, Jonas & Weisser, Johannes, 2011. "Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7," International Journal of Forecasting, Elsevier, vol. 27(2), pages 452-465.
    3. Loungani, Prakash, 2001. "How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth," International Journal of Forecasting, Elsevier, vol. 17(3), pages 419-432.
    4. Masahiro Ashiya, 2009. "Strategic bias and professional affiliations of macroeconomic forecasters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 120-130.
    5. Tilman Brück & Andreas Stephan, 2006. "Do Eurozone Countries Cheat with their Budget Deficit Forecasts?," Kyklos, Wiley Blackwell, vol. 59(1), pages 3-15, February.
    6. Loungani, Prakash & Stekler, Herman & Tamirisa, Natalia, 2013. "Information rigidity in growth forecasts: Some cross-country evidence," International Journal of Forecasting, Elsevier, vol. 29(4), pages 605-621.
    7. Olivier J. Blanchard & Daniel Leigh, 2013. "Growth Forecast Errors and Fiscal Multipliers," American Economic Review, American Economic Association, vol. 103(3), pages 117-120, May.
    8. Ager, P. & Kappler, M. & Osterloh, S., 2009. "The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach," International Journal of Forecasting, Elsevier, vol. 25(1), pages 167-181.
    9. Carlos Bowles & Roberta Friz & Veronique Genre & Geoff Kenny & Aidan Meyler & Tuomas Rautanen, 2007. "The ECB survey of professional forecasters (SPF) – A review after eight years’ experience," Occasional Paper Series 59, European Central Bank.
    10. Michael Artis & Massimiliano Marcellino, 2001. "Fiscal forecasting: The track record of the IMF, OECD and EC," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 20-36.
    11. Ashiya, Masahiro, 2003. "Testing the rationality of Japanese GDP forecasts: the sign of forecast revision matters," Journal of Economic Behavior & Organization, Elsevier, vol. 50(2), pages 263-269, February.
    12. Campbell, Sean D. & Sharpe, Steven A., 2009. "Anchoring Bias in Consensus Forecasts and Its Effect on Market Prices," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 369-390, April.
    13. Tong, Hui, 2007. "Disclosure standards and market efficiency: Evidence from analysts' forecasts," Journal of International Economics, Elsevier, vol. 72(1), pages 222-241, May.
    14. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    15. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    16. Ron Kasznik & Maureen F. McNichols, 2002. "Does Meeting Earnings Expectations Matter? Evidence from Analyst Forecast Revisions and Share Prices," Journal of Accounting Research, Wiley Blackwell, vol. 40(3), pages 727-759, June.
    17. Kenny, Geoff & Genre, Véronique & Bowles, Carlos & Friz, Roberta & Meyler, Aidan & Rautanen, Tuomas, 2007. "The ECB survey of professional forecasters (SPF) - A review after eight years' experience," Occasional Paper Series 59, European Central Bank.
    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. Jakub Rybacki, 2021. "Polish GDP forecast errors: a tale of inefficiency," Bank i Kredyt, Narodowy Bank Polski, vol. 52(2), pages 123-142.
    2. Dovern, Jonas & Weisser, Johannes, 2011. "Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7," International Journal of Forecasting, Elsevier, vol. 27(2), pages 452-465, April.
    3. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    4. Chang, Chia-Lin & de Bruijn, Bert & Franses, Philip Hans & McAleer, Michael, 2013. "Analyzing fixed-event forecast revisions," International Journal of Forecasting, Elsevier, vol. 29(4), pages 622-627.
    5. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    6. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
    7. Dovern, Jonas & Fritsche, Ulrich & Loungani, Prakash & Tamirisa, Natalia, 2015. "Information rigidities: Comparing average and individual forecasts for a large international panel," International Journal of Forecasting, Elsevier, vol. 31(1), pages 144-154.
    8. Jalles, João Tovar & Karibzhanov, Iskander & Loungani, Prakash, 2015. "Cross-country evidence on the quality of private sector fiscal forecasts," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 186-201.
    9. Dovern, Jonas & Fritsche, Ulrich & Loungani, Prakash & Tamirisa, Natalia, 2013. "Information Rigidities in Economic Growth Forecasts: Evidence from a Large International Panel," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79936, Verein für Socialpolitik / German Economic Association.
    10. Vereda, Luciano & Savignon, João & Gouveia da Silva, Tarciso, 2021. "A new method to assess the degree of information rigidity using fixed-event forecasts," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1576-1589.
    11. Zidong An & Joao Tovar Jalles, 2020. "On the performance of US fiscal forecasts: government vs. private information," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(2), pages 367-391, June.
    12. Sergey V. Smirnov & Daria A. Avdeeva, 2016. "Wishful Bias in Predicting Us Recessions: Indirect Evidence," HSE Working papers WP BRP 135/EC/2016, National Research University Higher School of Economics.
    13. Bruno Deschamps & Christos Ioannidis, 2014. "The Efficiency of Multivariate Macroeconomic Forecasts," Manchester School, University of Manchester, vol. 82(5), pages 509-523, September.
    14. Sergey V. Smirnov, 2014. "Predicting US Recessions: Does a Wishful Bias Exist?," HSE Working papers WP BRP 77/EC/2014, National Research University Higher School of Economics.
    15. Cronin, David & McInerney, Niall, 2023. "Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment," European Journal of Political Economy, Elsevier, vol. 76(C).
    16. Vasconcelos de Deus, Joseph David Barroso & de Mendonça, Helder Ferreira, 2017. "Fiscal forecasting performance in an emerging economy: An empirical assessment of Brazil," Economic Systems, Elsevier, vol. 41(3), pages 408-419.
    17. Xie, Zixiong & Hsu, Shih-Hsun, 2016. "Time varying biases and the state of the economy," International Journal of Forecasting, Elsevier, vol. 32(3), pages 716-725.
    18. Franses, Ph.H.B.F., 2019. "Professional Forecasters and January," Econometric Institute Research Papers EI2019-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    20. Gatti, Roberta & Lederman, Daniel & Islam, Asif M. & Nguyen, Ha & Lotfi, Rana & Emam Mousa, Mennatallah, 2024. "Data transparency and GDP growth forecast errors," Journal of International Money and Finance, Elsevier, vol. 140(C).

    More about this item

    Keywords

    GDP forecasting;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • 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:pra:mprapa:98952. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.