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Macroeconomic Forecasting in Poland: Lessons From the COVID-19 Outbreak

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  • Rybacki, Jakub
  • Gniazdowski, Michał

Abstract

The aim of this paper is to analyze the forecast errors of Polish professional forecasters under the COVID-19 crisis in 2020—based on the Parkiet competition. This analysis shows that after the initial disruption related to imposed lockdown in March and April, commercial economists were capable of lowering their forecasts errors of the industrial production and retail sales. On the other hand, the far worse performance has been seen in the case of the market variable; either the size of errors or the disagreement were elevated throughout the entirety of 2020. Furthermore, long-term forecasts that were produced during the first year of the pandemic have been characterized with visible inconsistencies (i.e., projections of economic growth were similar when forecasters either assumed a strong increase in unemployment or when they did not).

Suggested Citation

  • Rybacki, Jakub & Gniazdowski, Michał, 2021. "Macroeconomic Forecasting in Poland: Lessons From the COVID-19 Outbreak," MPRA Paper 107682, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:107682
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    References listed on IDEAS

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    1. Martin S Eichenbaum & Sergio Rebelo & Mathias Trabandt, 2021. "The Macroeconomics of Epidemics [Economic activity and the spread of viral diseases: Evidence from high frequency data]," Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5149-5187.
    2. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    3. Eicher, Theo S. & Kuenzel, David J. & Papageorgiou, Chris & Christofides, Charis, 2019. "Forecasts in times of crises," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1143-1159.
    4. Scotti, Chiara, 2016. "Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 1-19.
    5. Primiceri, Giorgio & Lenza, Michele, 2020. "How to Estimate a VAR after March 2020," CEPR Discussion Papers 15245, C.E.P.R. Discussion Papers.
    6. Jakub Rybacki, 2021. "Polish GDP forecast errors: a tale of inefficiency," Bank i Kredyt, Narodowy Bank Polski, vol. 52(2), pages 123-142.
    7. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    8. Zidong An & João Tovar Jalles & Prakash Loungani, 2018. "How well do economists forecast recessions?," International Finance, Wiley Blackwell, vol. 21(2), pages 100-121, June.
    9. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    10. Rybacki Jakub, 2020. "Macroeconomic forecasting in Poland: The role of forecasting competitions," Central European Economic Journal, Sciendo, vol. 7(54), pages 1-11, January.
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    More about this item

    Keywords

    GDP forecasting; Labor Market forecasts; COVID-19;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • 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

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