IDEAS home Printed from https://ideas.repec.org/a/liu/liucej/v22y2025i1p129-174.html

How the post-COVID-19 US economy lost and then regained momentum against the Eurozone economy

Author

Listed:
  • Pierre Rostan, Alexandra Rostan

Abstract

The objective of the paper is to assess and compare the resilience of the post-Covid US and Eurozone economies. Quarterly growth rates (annualized) of the Real GDP of US and the Eurozone are forecasted between Q4 2023 and Q4 2050. Two sets of forecasts are generated: forecasts using historical data including the pandemic (from Q4 1997 to Q3 2023) and not including the pandemic (from Q4 1997 to Q3 2019). The computation of the difference of their averages is an indicator of the resilience of the economies during the pandemic, the greater the difference the greater the resilience. Used as a benchmark, Eurozone (19 countries) shows a greater resilience to the Covid-19 pandemic (+0.27%) than the US (+0.17%) based on Q4 2023-Q4 2050 forecasts. However, the average of Q4 2023 - Q4 2050 quarterly (annualized) growth rate forecasts of the Eurozone is expected to be +0.87% with the Q4 1997 – Q3 2023 historical data whereas it is expected to be +1.49% for US. The US economy shows better prospects and greater momentum than the Eurozone economy.

Suggested Citation

  • Pierre Rostan, Alexandra Rostan, 2025. "How the post-COVID-19 US economy lost and then regained momentum against the Eurozone economy," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 22(1), pages 129-174, June.
    • Handle: RePEc:liu:liucej:v:22:y:2025:i:1:p:129-174
    as

    Download full text from publisher

    File URL: https://ejce.liuc.it/articles/ejce038.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Berger, Theo & Gençay, Ramazan, 2018. "Improving daily Value-at-Risk forecasts: The relevance of short-run volatility for regulatory quality assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 30-46.
    2. Theo Berger, 2016. "Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(5), pages 419-433, August.
    3. Theo Berger & Ramazan Gençay, 2020. "Short‐run wavelet‐based covariance regimes for applied portfolio management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 642-660, July.
    4. Chang-Tai Lee & Jin-Li Hu & Ming-Hsin Kung, 2022. "Economic Resilience in the Early Stage of the COVID-19 Pandemic: An Across-Economy Comparison," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    5. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    6. Ramazan Gencay & Nikola Gradojevic & Faruk Selcuk & Brandon Whitcher, 2010. "Asymmetry of information flow between volatilities across time scales," Quantitative Finance, Taylor & Francis Journals, vol. 10(8), pages 895-915.
    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. Berger, Theo & Czudaj, Robert L., 2020. "Commodity futures and a wavelet-based risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    2. Czudaj, Robert L., 2019. "Crude oil futures trading and uncertainty," Energy Economics, Elsevier, vol. 80(C), pages 793-811.
    3. Owadally, Iqbal & Jang, Chul & Clare, Andrew, 2021. "Optimal investment for a retirement plan with deferred annuities," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 51-62.
    4. Kearney, Fearghal & Shang, Han Lin & Sheenan, Lisa, 2019. "Implied volatility surface predictability: The case of commodity markets," Journal of Banking & Finance, Elsevier, vol. 108(C).
    5. Lily Liu, 2017. "Estimating Loss Given Default from CDS under Weak Identification," Supervisory Research and Analysis Working Papers RPA 17-1, Federal Reserve Bank of Boston.
    6. Detlefsen, Kai & Härdle, Wolfgang Karl, 2006. "Forecasting the term structure of variance swaps," SFB 649 Discussion Papers 2006-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Dauwe, Alexander & Moura, Marcelo L., 2011. "Forecasting the term structure of the Euro Market using Principal Component Analysis," Insper Working Papers wpe_233, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    8. Evangelos Salachas & Georgios P. Kouretas & Nikiforos T. Laopodis, 2024. "The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1018-1041, July.
    9. Caporale, Guglielmo Maria & Gil-Alana, Luis Alberiko & Poza, Carlos, 2022. "The COVID-19 pandemic and the degree of persistence of US stock prices and bond yields," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 118-123.
    10. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
    11. João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
    12. Adam Traczyk, 2013. "Financial integration and the term structure of interest rates," Empirical Economics, Springer, vol. 45(3), pages 1267-1305, December.
    13. Ang, Andrew & Piazzesi, Monika & Wei, Min, 2006. "What does the yield curve tell us about GDP growth?," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 359-403.
    14. Shigenori Shiratsuka, 2025. "Monetary Policy Effectiveness under the Ultra‐Low Interest Rate Environment: Evidence from Yield Curve Dynamics in Japan," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 87(1), pages 98-121, February.
    15. Hong, Zhiwu & Niu, Linlin & Zhang, Chen, 2022. "Affine arbitrage-free yield net models with application to the euro debt crisis," Journal of Econometrics, Elsevier, vol. 230(1), pages 201-220.
    16. Ioannis A. Venetis & Avgoustinos Ladas, 2023. "Co-movement and global factors in sovereign bond yields," Bulletin of Applied Economics, Risk Market Journals, vol. 10(2), pages 17-45.
    17. Thierry Moudiki & Frédéric Planchet & Areski Cousin, 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks," Risks, MDPI, vol. 6(1), pages 1-20, March.
    18. Mohamed Amine Boutabba & Yves Rannou, 2020. "Investor strategies and Liquidity Premia in the European Green Bond market," Post-Print hal-02544451, HAL.
    19. Lauren Stagnol, 2017. "Introducing global term structure in a risk parity framework," Working Papers hal-04141648, HAL.
    20. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.

    More about this item

    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

    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:liu:liucej:v:22:y:2025:i:1:p:129-174. 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: Laura Ballestra (email available below). General contact details of provider: https://edirc.repec.org/data/liuccit.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.