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The Predictive Content of U.S. Energy Information Administration Oil Market Forecasts

Author

Listed:
  • Garratt Anthony

    (Warwick Business School, University of Warwick)

  • Petrella Ivan

    (Esomas Department and Collegio Carlo Alberto, University of Turin; CEPR)

  • Zhang Yunyi

    (School of Management, China Institute for Studies in Energy Policy, Xiamen University)

Abstract

This paper investigates the information content of oil market forecasts produced by the U.S. Energy Information Administration (EIA). We evaluate the maximum informative forecast horizons for EIA projections of world and U.S. oil demand, supply, inventories, and prices. Our results show that U.S. forecasts are systematically more informative than their global counterparts, with content horizons extending up to six quarters for most U.S. variables. The information content embedded in EIA forecasts reflects both the agency's ability to track evolving market conditions and, particularly at short horizons, the incorporation of information that goes beyond simple trend extrapolation.

Suggested Citation

  • Garratt Anthony & Petrella Ivan & Zhang Yunyi, 2026. "The Predictive Content of U.S. Energy Information Administration Oil Market Forecasts," Working papers 104, Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    • Handle: RePEc:tur:wpapnw:104
    as

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    References listed on IDEAS

    as
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    4. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    5. Lady, George M., 2010. "Evaluating long term forecasts," Energy Economics, Elsevier, vol. 32(2), pages 450-457, March.
    6. Michael P. Clements, 2015. "Are Professional Macroeconomic Forecasters Able To Do Better Than Forecasting Trends?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 349-382, March.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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