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Forecasting the real price of oil in a changing world: A forecast combination approach

  • Baumeister, Christiane
  • Kilian, Lutz

The U.S. Energy Information Administration (EIA) regularly publishes monthly and quarterly forecasts of the price of crude oil for horizons up to two years, which are widely used by practitioners. Traditionally, such out-of-sample forecasts have been largely judgmental, making them difficult to replicate and justify. An alternative is the use of real-time econometric oil price forecasting models. We investigate the merits of constructing combinations of six such models. Forecast combinations have received little attention in the oil price forecasting literature to date. We demonstrate that over the last 20 years suitably constructed real-time forecast combinations would have been systematically more accurate than the no-change forecast at horizons up to 6 quarters or 18 months. MSPE reduction may be as high as 12% and directional accuracy as high as 72%. The gains in accuracy are robust over time. In contrast, the EIA oil price forecasts not only tend to be less accurate than no-change forecasts, but are much less accurate than our preferred forecast combination. Moreover, including EIA forecasts in the forecast combination systematically lowers the accuracy of the combination forecast. We conclude that suitably constructed forecast combinations should replace traditional judgmental forecasts of the price of oil.

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Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2013/11.

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Date of creation: 2013
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Handle: RePEc:zbw:cfswop:201311
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  1. repec:taf:jnlbes:v:30:y:2012:i:2:p:326-336 is not listed on IDEAS
  2. Menzie D. Chinn & Olivier Coibion, 2010. "The Predictive Content of Commodity Futures," Working Papers 89, Department of Economics, College of William and Mary.
  3. Baumeister, Christiane & Kilian, Lutz, 2011. "Real-Time Forecasts of the Real Price of Oil," CEPR Discussion Papers 8414, C.E.P.R. Discussion Papers.
  4. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  5. Baumeister, Christiane & Kilian, Lutz, 2013. "Are product spreads useful for forecasting? An empirical evaluation of the Verleger hypothesis," CFS Working Paper Series 2013/09, Center for Financial Studies (CFS).
  6. Alquist, Ron & Kilian, Lutz, 2007. "What Do We Learn from the Price of Crude Oil Futures?," CEPR Discussion Papers 6548, C.E.P.R. Discussion Papers.
  7. Philip K Verleger, 2011. "The Margin, Currency, and the Price of Oil," Business Economics, Palgrave Macmillan, vol. 46(2), pages 71-82, April.
  8. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, June.
  9. Christiane Baumeister & Lutz Kilian, 2014. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," IMF Economic Review, Palgrave Macmillan, vol. 62(1), pages 119-145, April.
  10. Knetsch, Thomas A., 2006. "Forecasting the price of crude oil via convenience yield predictions," Discussion Paper Series 1: Economic Studies 2006,12, Deutsche Bundesbank, Research Centre.
  11. Christiane Baumeister & Lutz Kilian, 2013. "What Central Bankers Need to Know about Forecasting Oil Prices," Working Papers 13-15, Bank of Canada.
  12. Kilian, Lutz & Murphy, Daniel P, 2010. "The Role of Inventories and Speculative Trading in the Global Market for Crude Oil," CEPR Discussion Papers 7753, C.E.P.R. Discussion Papers.
  13. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-69, June.
  14. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  15. Pesaran, M.H. & Timmermann, A., 2006. "Testing Dependence Among Serially Correlated Multi-category Variables," Cambridge Working Papers in Economics 0648, Faculty of Economics, University of Cambridge.
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