IDEAS home Printed from https://ideas.repec.org/a/arp/bmerar/2022p51-61.html
   My bibliography  Save this article

How Has the COVID-19 Pandemic Affected GDP Growth?-Empirical Study on USA and China-

Author

Listed:
  • Ahmed N. K. Alfarra

    (Faculty of Economics and Administrative Sciences, The Islamic University, Gaza, 108, Palestine School of Management, Harbin Institute of Technology, Harbin 150001, China)

  • Ahmed Hagag

    (Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt)

Abstract

This paper anticipates trends in the digital economy during a COVID-19 epidemic worldwide. The United States and China are considered the world’s largest economies and have attempted to transition to fully digital economies over the last few years. Therefore, this paper used the auto-regressive integrated moving average (ARIMA) model and the gross domestic product (GDP) for the USA and China over the period 1960-2019. As we arrive at the peak of the COVID-19 pandemic, one of the most squeezing questions confronting us is: How has the COVID-19 crisis affected the USA and China’s GDP growth? The results have shown first that the GDP growth for both years 2020 and 2021 are approximately 6% and 10% for the USA and China, respectively. Second, the COVID-19 pandemic cannot influence the countries that depend on technology and the digital economy. It can be seen that technology is playing a very significant role in our daily life and nations’ economies.

Suggested Citation

  • Ahmed N. K. Alfarra & Ahmed Hagag, 2022. "How Has the COVID-19 Pandemic Affected GDP Growth?-Empirical Study on USA and China-," Business, Management and Economics Research, Academic Research Publishing Group, vol. 8(3), pages 51-61, 09-2022.
    • Handle: RePEc:arp:bmerar:2022:p:51-61
    DOI: https://doi.org/10.32861/bmer.83.51.61
    as

    Download full text from publisher

    File URL: https://www.arpgweb.com/pdf-files/bmer8(3)51-61.pdf
    Download Restriction: no
    File URL: https://www.arpgweb.com/journal/8/archive/09-2022/3/8
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.32861/bmer.83.51.61?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Singh, Sarbjit & Parmar, Kulwinder Singh & Kumar, Jatinder & Makkhan, Sidhu Jitendra Singh, 2020. "Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    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. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    2. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    3. Hafize Nurgul Durmus Senyapar & Ahmet Aksoz, 2024. "Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions," Sustainability, MDPI, vol. 16(7), pages 1-23, April.
    4. Aldila, Dipo, 2020. "Analyzing the impact of the media campaign and rapid testing for COVID-19 as an optimal control problem in East Java, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    5. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    6. Singh, Sarbjit & Parmar, Kulwinder Singh & Makkhan, Sidhu Jitendra Singh & Kaur, Jatinder & Peshoria, Shruti & Kumar, Jatinder, 2020. "Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    7. Batistela, Cristiane M. & Correa, Diego P.F. & Bueno, Átila M & Piqueira, José Roberto C., 2021. "SIRSi compartmental model for COVID-19 pandemic with immunity loss," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Charu Arora & Poras Khetarpal & Saket Gupta & Nuzhat Fatema & Hasmat Malik & Asyraf Afthanorhan, 2023. "Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    9. Duan, Huiming & Nie, Weige, 2022. "A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    10. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    11. María del Carmen Valls Martínez & Pedro Antonio Martín Cervantes, 2021. "Testing the Resilience of CSR Stocks during the COVID-19 Crisis: A Transcontinental Analysis," Mathematics, MDPI, vol. 9(5), pages 1-24, March.
    12. Dinesh K. Sharma & H. S. Hota & Kate Brown & Richa Handa, 2022. "Integration of genetic algorithm with artificial neural network for stock market forecasting," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 828-841, June.
    13. Claudio Barbiellini Amidei & Ugo Fedeli & Nicola Gennaro & Laura Cestari & Elena Schievano & Manuel Zorzi & Paolo Girardi & Veronica Casotto, 2023. "Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared," IJERPH, MDPI, vol. 20(11), pages 1-13, May.
    14. Perone, G., 2020. "Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/18, HEDG, c/o Department of Economics, University of York.
    15. Nathan Zavanelli, 2023. "Wavelet Analysis for Time Series Financial Signals via Element Analysis," Papers 2301.13255, arXiv.org.
    16. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    17. Daren Zhao & Huiwu Zhang & Qing Cao & Zhiyi Wang & Sizhang He & Minghua Zhou & Ruihua Zhang, 2022. "The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-18, February.
    18. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    19. Yeşilkanat, Cafer Mert, 2020. "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    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:arp:bmerar:2022:p:51-61. 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: Managing Editor (email available below). General contact details of provider: http://arpgweb.com/index.php?ic=journal&journal=8&info=aims .

    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.