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Measuring the Resilience to the Covid-19 Pandemic of Eurozone Economies with Their 2050 Forecasts

Author

Listed:
  • Pierre Rostan

    (American University of Iraq Baghdad)

  • Alexandra Rostan

    (American University of Iraq Baghdad)

  • John Wall

    (American University of Iraq Baghdad)

Abstract

This paper measures the resilience of Eurozone economies following the economic shock of the Covid-19 pandemic that hit the global economy. Q2 2022 to Q4 2050 real GDP forecasts of 17 countries of the Eurozone are generated with wavelet analysis using historical real GDP quarterly data excluding the pandemic (Q4 1994 up to Q3 2019) and including the pandemic (Q4 1994 up to the Q1 2022). The means of the Q2 2022 up to Q4 2050 forecasts of the quarterly growth rates (annualized) are computed with the two sets of historical data including and excluding the pandemic. The difference in mean forecasts measures the resilience of economies during the pandemic, the more positive the difference, the stronger the resilience. Based on this indicator of resilience, among high GDP countries, Italy is the most resilient economy towards the Covid-19 pandemic (ranked No 1 among 17 countries), Germany is the least (No 16). Among medium GDP countries, Finland is the most resilient economy towards the Covid-19 pandemic (ranked No 5), Greece is the least (No 14). Among low GDP countries, Malta is the most resilient economy towards the Covid-19 pandemic (ranked No 6), Latvia is the least (No 17).

Suggested Citation

  • Pierre Rostan & Alexandra Rostan & John Wall, 2024. "Measuring the Resilience to the Covid-19 Pandemic of Eurozone Economies with Their 2050 Forecasts," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1137-1157, March.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:3:d:10.1007_s10614-023-10425-z
    DOI: 10.1007/s10614-023-10425-z
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    References listed on IDEAS

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    1. Baillie, Richard T. & Bollerslev, Tim, 1992. "Prediction in dynamic models with time-dependent conditional variances," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 91-113.
    2. Pierre Rostan & Alexandra Rostan, 2018. "The versatility of spectrum analysis for forecasting financial time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 327-339, April.
    3. Marco Gallegati & Mauro Gallegati & James B. Ramsey & Willi Semmler, 2017. "Long waves in prices: new evidence from wavelet analysis," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 11(1), pages 127-151, January.
    4. Pierre Rostan & Alexandra Rostan, 2019. "When will European Muslim population be majority and in which country?," PSU Research Review, Emerald Group Publishing Limited, vol. 3(2), pages 123-144, August.
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    More about this item

    Keywords

    GDP; Spectral analysis; ARIMA; Forecasting; Eurozone economies;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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