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Oil Price Forecasts for the Long-Term: Expert Outlooks, Models, or Both?

Author

Listed:
  • Jean-Thomas Bernard

    (Department of Economics, University of Ottawa)

  • Lynda Khalaf

    (Carleton University)

  • Maral Kichian

    (Graduate School of Public and International Affairs, University of Ottawa)

  • Clement Yelou

    (Laval University)

Abstract

Expert outlooks on the future path of oil prices are often relied on by industry participants and policymaking bodies for their forecasting needs. Yet little attention has been paid to the extent to which these are accurate. Using the regular publications by the Energy Information Administration (EIA), we examine the accuracy of annual recursive oil price forecasts generated by the National Energy Modeling System model of the Agency for forecast horizons of up to 15 years. Our results reveal that the EIA model is quite successful at beating the benchmark random walk model, but only at either end of the forecast horizons. We also show that, for the longer horizons, simple econometric forecasting models often produce similar if not better accuracy than the EIA model. Among these, time-varying specifications generally also exhibit stability in their forecast performance. Finally, while combining forecasts does not change the overall patterns, some additional accuracy gains are obtained at intermediate horizons, and in some cases forecast performance stability is also achieved.

Suggested Citation

  • Jean-Thomas Bernard & Lynda Khalaf & Maral Kichian & Clement Yelou, 2015. "Oil Price Forecasts for the Long-Term: Expert Outlooks, Models, or Both?," Working Papers 1510E, University of Ottawa, Department of Economics.
    • Handle: RePEc:ott:wpaper:1510e
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    2. Chu, Pyung Kun & Hoff, Kristian & Molnár, Peter & Olsvik, Magnus, 2022. "Crude oil: Does the futures price predict the spot price?," Research in International Business and Finance, Elsevier, vol. 60(C).
    3. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    4. Gonzalo Cortazar & Philip Liedtke & Hector Ortega & Eduardo S. Schwartzd, 2022. "Time-Varying Term Structure of Oil Risk Premia," The Energy Journal, , vol. 43(5), pages 71-92, September.
    5. Piersanti, Giovanni & Piersanti, Mirko & Cicone, Antonio & Canofari, Paolo & Di Domizio, Marco, 2020. "An inquiry into the structure and dynamics of crude oil price using the fast iterative filtering algorithm," Energy Economics, Elsevier, vol. 92(C).
    6. Knut Are Aastveit & Jamie L. Cross & Herman K. van Dijk, 2023. "Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 523-537, April.
    7. Zied Ftiti & Kais Tissaoui & Sahbi Boubaker, 2022. "On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach," Annals of Operations Research, Springer, vol. 313(2), pages 915-943, June.
    8. Gonzalo Cortazar & Cristobal Millard & Hector Ortega & Eduardo S. Schwartz, 2019. "Commodity Price Forecasts, Futures Prices, and Pricing Models," Management Science, INFORMS, vol. 65(9), pages 4141-4155, September.

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    Keywords

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    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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