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Extreme Event Modelling and Forecasting: Empirical Evidence from Predicting GDP and Unemployment in the USA

Author

Listed:
  • R. Shankar

    (Alliance Manchester Business School, University of Manchester, Manchester M15 6PB, UK)

  • Azzam Alroomi

    (Faculty of Business Studies, Arab Open University, Al Ardiya 92400, Kuwait)

  • V. Bougioukos

    (Richmond Business School, Richmond American University London, London W4 5AN, UK
    London Global Gateway, The University of Notre Dame (USA) in England, London SW1Y4HG, UK)

  • K. Nikolopoulos

    (Durham University Business School & IHRR, Durham University, Durham DH1 1SL, UK)

Abstract

This paper contributes to the stream of literature on extreme event modelling and forecasting by comparing various forecasting methods for predicting extreme movements in GDP and unemployment in the United States. The data were obtained from multiple open sources for the USA, including CNBC, the U.S. National Library of Medicine, the National Institutes of Health, the Centres for Disease Control and Prevention, the Bureau of Transportation Statistics site, Investing Com, the U.S. Bureau of Labour Statistics, Yahoo Finance, The Balance and Wikipedia. The research focuses on identifying the optimal forecasting method between Machine Learning and time-series forecasting algorithms, for predicting extreme values of GDP and unemployment, accounting for natural disasters and industrial and economic factors. The statistical and analytical insights derived from this study, if used judiciously, can inform policymaking and planning.

Suggested Citation

  • R. Shankar & Azzam Alroomi & V. Bougioukos & K. Nikolopoulos, 2026. "Extreme Event Modelling and Forecasting: Empirical Evidence from Predicting GDP and Unemployment in the USA," Forecasting, MDPI, vol. 8(3), pages 1-12, June.
    • Handle: RePEc:gam:jforec:v:8:y:2026:i:3:p:46-:d:1962659
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