IDEAS home Printed from https://ideas.repec.org/p/tur/wpapnw/104.html

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

    Download full text from publisher

    File URL: https://www.bemservizi.unito.it/repec/tur/wpapnw/m104.pdf
    File Function: First version, 2026
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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

    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:tur:wpapnw:104. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Daniele Pennesi (email available below). General contact details of provider: https://edirc.repec.org/data/dstorit.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.