Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-28T01:07:10.206Z Has data issue: false hasContentIssue false
  • English
  • Français

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: LyndaKhalaf@cunet.carleton.ca
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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

REFERENCES

Alquist, Ron and Kilian, Lutz (2010) What do we learn from the price of crude oil futures? Journal of Applied Econometrics 25, 539–573.Google Scholar
Alquist, Ron, Kilian, Lutz and Vigfusson, Robert J. (2013) Forecasting the price of oil. In Elliott, G., and Timmermann, A. (eds.), Handbook of Economic Forecasting, vol. 2, pp. 427–507. Amsterdam, North-Holland: Elsevier.Google Scholar
Baumeister, Christiane and Kilian, Lutz (2012) Real-time forecasts of the real price of oil. Journal of Business and Economic Statistics 30 (2), 326–336.Google Scholar
Baumeister, Christiane and Kilian, Lutz (2014a) What central bankers need to know about forecasting oil prices. International Economic Review 55, 869–889.CrossRefGoogle Scholar
Baumeister, Christiane and Kilian, Lutz (2014b) Forecasting the real price of oil in a changing world: A forecast combination approach. Journal of Business and Economic Statistics 33 (3), 338–351.Google Scholar
Baumeister, Christiane, Kilian, Lutz and Zhou, Xiaoqing (2013) Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis. Bank of Canada working paper 2013-25.Google Scholar
Baumeister, Christiane, Guerin, Pierre and Kilian, Lutz (2015) Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. International Journal of Forecasting 32, 238–252.Google Scholar
Baumeister, Christiane, Kilian, Lutz and Lee, Thomas (2014) Are there gains from pooling real-time oil price forecasts? Energy Economics 46, S33–S43.Google Scholar
Bernard, Jean-Thomas, Khalaf, Lynda, Kichian, Maral and MacMahon, Sebastien (2015) The convenience yield and the informational content of the oil futures price. The Energy Journal 36, 29–46.Google Scholar
Bernard, Jean-Thomas, Khalaf, Lynda and Kichian, Maral (2012) An identification-robust test for time-varying parameters in the dynamics of energy prices. Journal of Applied Econometrics 27 (4), 603–624.Google Scholar
Chen, Shiu-Sheng (2014) Forecasting crude oil price movements with oil-sensitive stocks. Economic Inquiry 52 (2), 830–844.Google Scholar
Chinn, Menzie and Coibion, Olivier (2013) The predictive content of commodity futures. Journal of Futures Markets 34 (7), 607–636.Google Scholar
Dvir, Eyal and Rogoff, Kenneth (2010) Three Epochs of Oil. NBER working paper no. 14927.Google Scholar
Fischer, Carolyn, Herrnstadt, Evan and Morgenstern, Richard (2008) Understanding Errors in EIA Projections of Energy Demand. Resources for the Future discussion paper 07-54.Google Scholar
Hamilton, James D. (2009) Understanding crude oil prices. The Energy Journal 30, 179–206.CrossRefGoogle Scholar
Hamilton, James D. (2011) Nonlinearities and the macroeconomic effects of oil prices. Macroeconomic Dynamics 15, 364–378.Google Scholar
Hansen, Bruce E. (2007) Least squares model averaging. Econometrica 75 (4), 1175–1189.Google Scholar
Hansen, Bruce E. (2008) Least-squares forecast averaging. Journal of Econometrics 146 (2), 342–350.Google Scholar
Huntington, Hillard G. (2011) Backcasting US oil demand over a turbulent decade. Energy Policy 39, 5674–5680.Google Scholar
Kilian, Lutz (2008) The economic effects of energy price shocks. Journal of Economic Literature 46, 871–909.Google Scholar
Kilian, Lutz (2014) Oil price shocks: Causes and consequences. Annual Review of Resource Economics 6, 133–154.Google Scholar
Kilian, Lutz and Vigfusson, Robert J. (2011) Nonlinearities in the oil price-output relationship. Macroeconomic Dynamics 15, 337–363.CrossRefGoogle Scholar
Kilian, Lutz and Vigfusson, Robert J. (2013) Do oil prices help forecast U.S. real GDP? The role of nonlinearities and asymmetries. Journal of Business and Economic Statistics 31, 78–93.Google Scholar
Kilian, Lutz and Murphy, Dan (2014) The role of inventories and speculative trading in the global market for crude oil. Journal of Applied Econometrics 29, 454–478.Google Scholar
Kim, Chiang-Jin and Nelson, Charles R. (1999) State-Space Models with Regime-Switching. USA: MIT Press.Google Scholar
Lynch, Michael (2002) Forecasting oil supply: Theory and practice. The Quarterly Review of Economics and Finance 42, 373–389.Google Scholar
Manthy, R. S. (1978) Natural Resource Commodities: A Century of Statistics. USA: John Hopkins Press.Google Scholar
O'Neill, Brian and Desai, Mausam (2005) Accuracy of past projections of US energy consumption. Energy Policy 33, 979–993.Google Scholar
Pesaran, M. Hashem and Timmermann, Allan (2009) Testing dependence among serially correlated multicategory variables. Journal of the American Statistical Association 104, 325–337.Google Scholar
Pindyck, Robert S. (1999) The long run evolution of energy prices. The Energy Journal 20, 1–27.Google Scholar
Ravazzolo, Francesco and Rothman, Philip (2013) Oil and U.S. GDP: A real-time out-of-sample examination. Journal of Money, Credit, and Banking 45, 449–463.Google Scholar
Sanders, Dwight, Manfredo, Mark and Boris, Kieth (2009) Evaluating information in multiple-horizon forecasts: The DOE's energy price forecasts. Energy Economics 31, 189–196.Google Scholar
Slade, E. Margaret (1982) Trends in natural-resource commodity prices: An analysis of the time domain. Journal of Environmental Economics and Management 9 (2), 122–137.Google Scholar
Winebrake, James and Sakva, Denys (2006) An evaluation of errors in US energy forecasts: 1982–2003. Energy Policy 34, 3475–3483.Google Scholar