IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/108912.html

GDP Forecast of the Biggest GCC Economies Using ARIMA

Author

Listed:
  • Youssef, Jamile
  • Ishker, Nermeen
  • Fakhreddine, Nour

Abstract

Gulf Cooperation Council (GCC) members are considered one of the fastest growing economies. This paper aims to empirically forecast the economic activity of the vastest GCC countries: Qatar, Saudi Arabia, and the United Arab Emirates. An Auto-Regressive Moving Average (ARIMA) model for the three countries Gross Domestic Product is obtained using the Box-Jenkins methodology during the 1980 - 2020 period. The appropriate models for the three economies are of ARIMA (0,2,1), the forecasts are at a 95% confidence level and predicts a growth in the three countries for the upcoming five years.

Suggested Citation

  • Youssef, Jamile & Ishker, Nermeen & Fakhreddine, Nour, 2021. "GDP Forecast of the Biggest GCC Economies Using ARIMA," MPRA Paper 108912, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:108912
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/108912/1/MPRA_paper_108912.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ning, Wei & Kuan-jiang, Bian & Zhi-fa, Yuan, 2010. "Analysis and Forecast of Shaanxi GDP Based on the ARIMA Model," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 2(01), pages 1-4, January.
    2. Edward E. Leamer, 2009. "Macroeconomic Patterns and Stories," Springer Books, Springer, number 978-3-540-46389-4, March.
    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. Wolfgang Polasek, 2010. "Dating and Exploration of the Business Cycle in Iceland," Working Paper series 13_10, Rimini Centre for Economic Analysis.
    2. DeMartino, George & Grabel, Ilene & Scoones, Ian, 2024. "Economics for an uncertain world," World Development, Elsevier, vol. 173(C).
    3. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    4. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237, April.
    5. Guilhem Lecouteux, 2022. "The Homer economicus narrative: from cognitive psychology to individual public policies," Working Papers hal-03791951, HAL.
    6. Ratcliff, Ryan, 2013. "The “probability of recession”: Evaluating probabilistic and non-probabilistic forecasts from probit models of U.S. recessions," Economics Letters, Elsevier, vol. 121(2), pages 311-315.
    7. Mark Hoekstra & Steven L. Puller & Jeremy West, 2017. "Cash for Corollas: When Stimulus Reduces Spending," American Economic Journal: Applied Economics, American Economic Association, vol. 9(3), pages 1-35, July.
    8. Edward E. Leamer, 2010. "Tantalus on the Road to Asymptopia," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 31-46, Spring.
    9. George F. DeMartino, 2021. "The specter of irreparable ignorance: counterfactuals and causality in economics," Review of Evolutionary Political Economy, Springer, vol. 2(2), pages 253-276, July.
    10. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Mr. Ashoka Mody, 2009. "From Bear Stearns to Anglo Irish: How Eurozone Sovereign Spreads Related to Financial Sector Vulnerability," IMF Working Papers 2009/108, International Monetary Fund.
    12. Raaj Kishore Biswas & Enamul Kabir & Refat Bin Reza Rafi, 2019. "Investment in Research and Development Compared to Military Expenditure: Is Research Worthwhile?," Defence and Peace Economics, Taylor & Francis Journals, vol. 30(7), pages 846-857, November.
    13. Guilhem Lecouteux, 2023. "The Homer economicus narrative: from cognitive psychology to individual public policies," Journal of Economic Methodology, Taylor & Francis Journals, vol. 30(2), pages 176-187, April.
    14. Atif Mian & Amir Sufi, 2011. "House Prices, Home Equity-Based Borrowing, and the US Household Leverage Crisis," American Economic Review, American Economic Association, vol. 101(5), pages 2132-2156, August.
    15. repec:ejw:journl:v:17:y:2020:i:1:p:228-241 is not listed on IDEAS
    16. Yuliya Lovcha & Alejandro Perez-Laborda & Luis Gil-Alana, 2018. "On the invertibility of seasonally adjusted series," Computational Statistics, Springer, vol. 33(1), pages 443-465, March.
    17. Atif Mian & Amir Sufi, 2010. "The Great Recession: Lessons from Microeconomic Data," American Economic Review, American Economic Association, vol. 100(2), pages 51-56, May.
    18. Moahmed Hassan, Hisham & Haleeb, Amin, 2020. "Modelling GDP for Sudan using ARIMA," MPRA Paper 101207, University Library of Munich, Germany.
    19. Edward E. Leamer, 2024. "Data patterns that reliably precede US recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2522-2539, November.
    20. Sjoerd Beugelsdijk & Allan Bird, 2025. "How to avoid a desk reject: do’s and don’ts," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 56(3), pages 301-310, April.
    21. Jennifer Castle & David Hendry, 2016. "Policy Analysis, Forediction, and Forecast Failure," Economics Series Working Papers 809, University of Oxford, Department of Economics.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East

    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:108912. 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.