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The drivers of oil prices: the usefulness and limitations of non-structural models, supply-demand frameworks, and informal approaches

Author

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  • Fattouh, Bassam

    (School of Oriental and African Studies (SOAS), University of London, and Senior Research Fellow at the Oxford Institute for Energy Studies (OIES).)

Abstract

This paper discusses three main approaches for analysing oil prices: non-structural models, the supplydemand framework, and the informal approach. Each approach emphasises a certain set of drivers of oil prices. While non-structural models rest on the theory of exhaustible resources, the supply-demand framework uses behavioural equations that link oil demand and supply to its various determinants. The informal approach focuses on the specifics of oil market history in explaining oil prices. Although all approaches provide useful insights on how the world oil market functions, they suffer from major limitations especially when used for long-term projections. Thus, pushing hard for policies based on such projections defeats the purpose of such models and may result in misguided policies.

Suggested Citation

  • Fattouh, Bassam, 2007. "The drivers of oil prices: the usefulness and limitations of non-structural models, supply-demand frameworks, and informal approaches," EIB Papers 6/2007, European Investment Bank, Economics Department.
    • Handle: RePEc:ris:eibpap:2007_006
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    Cited by:

    1. Okullo, Samuel J. & Reynès, Frédéric, 2011. "Can reserve additions in mature crude oil provinces attenuate peak oil?," Energy, Elsevier, vol. 36(9), pages 5755-5764.
    2. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting the diffusion of renewable electricity considering the impact of policy and oil prices: The case of South Korea," Applied Energy, Elsevier, vol. 197(C), pages 29-39.
    3. Cashin, Paul & Mohaddes, Kamiar & Raissi, Maziar & Raissi, Mehdi, 2014. "The differential effects of oil demand and supply shocks on the global economy," Energy Economics, Elsevier, vol. 44(C), pages 113-134.
    4. Abdel M. Zellou & John T. Cuddington, 2012. "Trends and Super Cycles in Crude Oil and Coal Prices," Working Papers 2012-10, Colorado School of Mines, Division of Economics and Business.
    5. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    6. Mohaddes, Kamiar & Pesaran, M. Hashem, 2016. "Country-specific oil supply shocks and the global economy: A counterfactual analysis," Energy Economics, Elsevier, vol. 59(C), pages 382-399.
    7. Nourah Al†Yousef, 2018. "Fundamentals and Oil Price Behaviour: New Evidence from Co†integration Tests with Structural Breaks and Granger Causality Tests," Australian Economic Papers, Wiley Blackwell, vol. 57(1), pages 1-18, March.
    8. Wajdi Hamza Dawod Alredany, 2018. "A Regression Analysis of Determinants Affecting Crude Oil Price," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 110-119.
    9. D'Ecclesia, Rita L. & Magrini, Emiliano & Montalbano, Pierluigi & Triulzi, Umberto, 2014. "Understanding recent oil price dynamics: A novel empirical approach," Energy Economics, Elsevier, vol. 46(S1), pages 11-17.
    10. Waisman, Henri & Rozenberg, Julie & Sassi, Olivier & Hourcade, Jean-Charles, 2012. "Peak Oil profiles through the lens of a general equilibrium assessment," Energy Policy, Elsevier, vol. 48(C), pages 744-753.
    11. Sofia Berdysheva & Svetlana Ikonnikova, 2021. "The Energy Transition and Shifts in Fossil Fuel Use: The Study of International Energy Trade and Energy Security Dynamics," Energies, MDPI, vol. 14(17), pages 1-26, August.
    12. Veniamin Todorov, 2022. "Exogenous Macroeconomic Shocks As Contemporary Business Cycle Determinants," Economic Archive, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 3 Year 20, pages 3-17.
    13. Bentley, Roger & Bentley, Yongmei, 2015. "Explaining the price of oil 1971–2014 : The need to use reliable data on oil discovery and to account for ‘mid-point’ peak," Energy Policy, Elsevier, vol. 86(C), pages 880-890.
    14. Hammoudeh, Shawkat & Mokni, Khaled & Ben-Salha, Ousama & Ajmi, Ahdi Noomen, 2021. "Distributional predictability between oil prices and renewable energy stocks: Is there a role for the COVID-19 pandemic?," Energy Economics, Elsevier, vol. 103(C).
    15. Khan, Muhammad Imran & Yasmeen, Tabassam & Shakoor, Abdul & Khan, Niaz Bahadur & Muhammad, Riaz, 2017. "2014 oil plunge: Causes and impacts on renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 609-622.
    16. Berk, Istemi & Çam, Eren, 2020. "The shift in global crude oil market structure: A model-based analysis of the period 2013–2017," Energy Policy, Elsevier, vol. 142(C).
    17. Chavez-Rodriguez, Mauro F. & Szklo, Alexandre & de Lucena, Andre Frossard Pereira, 2015. "Analysis of past and future oil production in Peru under a Hubbert approach," Energy Policy, Elsevier, vol. 77(C), pages 140-151.
    18. Berk, Istemi & Çam , Eren, 2019. "The Shift in Global Crude Oil Market Structure: A model-based analysis of the period 2013–2017," EWI Working Papers 2019-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).

    More about this item

    Keywords

    Oil prices; forecasting models;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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