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OIL PRICE FORECASTS FOR THE LONG TERM: EXPERT OUTLOOKS, MODELS, OR BOTH?

Published online by Cambridge University Press:  06 June 2017

Jean-Thomas Bernard ,
Lynda Khalaf ,
Maral Kichian  and
Clement Yelou
Jean-Thomas Bernard
Affiliation:
University of Ottawa
Lynda Khalaf*
Affiliation:
Carleton University
Maral Kichian
Affiliation:
University of Ottawa
Clement Yelou
Affiliation:
CREATE, Laval University
*
Address correspondence to: Lynda Khalaf, Economics Department, Carleton University, Loeb Building 1125 Colonel By Drive, Ottawa, Ontario, K1S 5B6, Canada; e-mail: [email protected]
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Abstract

Little is known about the accuracy of expert outlooks, so heavily relied upon by industry participants and policy makers, regarding the future path of oil prices. 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 outperforms the benchmark random walk model around the two ends of the forecast horizon spectrum. Additionally, at the longer horizons, simple econometric forecasting models often produce similar, if not better accuracy than the EIA model. Time varying such specifications generally also exhibit stability in their forecast performance. Finally, although 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.

Keywords

Oil priceexpert outlookslong run forecastingforecast combinations
Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Special Issue 3: Dynamics of Oil and Commodities Prices , April 2018 , pp. 581 - 599
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

Thanks to the editors, three anonymous referees, participants of the Bank of Canada 2015 commodities workshop, and to Christiane Baumeister for comments and suggestions. This work was supported by the Institut de Finance Mathématique de Montréal [IFM2], the Social Sciences and Humanities Research Council of Canada, and the Fonds FQRSC (Government of Québec).

References

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