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

Anthony Garratt (), Ivan Petrella () and Yunyi Zhang ()
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Anthony Garratt: Warwick Business School, University of Warwick
Ivan Petrella: Esomas Department and Collegio Carlo Alberto, University of Turin; CEPR
Yunyi Zhang: School of Management, China Institute for Studies in Energy Policy, Xiamen University

No 104, Working papers from Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino

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.

Keywords: EIA Forecasts; Oil Market; Forecast Horizon; Forecast Path; Non-convergent Forecasts. (search for similar items in EconPapers)
JEL-codes: C32 C53 E37 Q47 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2026-03
New Economics Papers: this item is included in nep-ene and nep-for
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