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Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach

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Author Info

  • Christiane Baumeister
  • Lutz Kilian

Abstract

The U.S. Energy Information Administration regularly publishes short-term forecasts of the price of crude oil. Traditionally, such out-of-sample forecasts have been largely judgmental, making them difficult to replicate and justify, and not particularly successful when compared with naïve no-change forecasts, as documented in Alquist, Kilian and Vigfusson (2013). Recently, a number of alternative econometric oil price forecasting models have been introduced in the literature and shown to be more accurate than the nochange forecast of the real price of oil. We investigate the merits of constructing realtime forecast combinations of six such models with weights that reflect the recent forecasting success of each model. Forecast combinations are promising for four reasons. First, even the most accurate forecasting models do not work equally well at all times. Second, some forecasting models work better at short horizons and others at longer horizons. Third, even the forecasting model with the lowest mean-squared prediction error (MSPE) may potentially be improved by incorporating information from other models with higher MSPEs. Fourth, one can think of forecast combinations as providing insurance against possible model misspecification and smooth structural change. We demonstrate that over the past 20 years suitably constructed real-time forecast combinations would have been more accurate than the no-change forecast at every horizon up to two years. Relative to the no-change forecast, forecast combinations reduce the MSPE by up to 18 per cent. They also have statistically significant directional accuracy as high as 77 per cent. We conclude that suitably constructed forecast combinations should replace traditional judgmental forecasts of the price of oil.

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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 13-28.

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Length: 32 pages
Date of creation: 2013
Date of revision:
Handle: RePEc:bca:bocawp:13-28

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Keywords: Econometric and statistical methods; International topics;

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References

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  1. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
  2. Christiane Baumeister & Lutz Kilian, 2013. "What Central Bankers Need to Know about Forecasting Oil Prices," Working Papers 13-15, Bank of Canada.
  3. Kilian, Lutz, 2006. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," CEPR Discussion Papers 5994, C.E.P.R. Discussion Papers.
  4. Baumeister, Christiane & Kilian, Lutz & Zhou, Xiaoqing, 2013. "Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis," CEPR Discussion Papers 9572, C.E.P.R. Discussion Papers.
  5. Kilian, Lutz & Murphy, Dan, 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.
  6. Menzie D. Chinn & Olivier Coibion, 2010. "The Predictive Content of Commodity Futures," NBER Working Papers 15830, National Bureau of Economic Research, Inc.
  7. Thomas A. Knetsch, 2007. "Forecasting the price of crude oil via convenience yield predictions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 527-549.
  8. repec:taf:jnlbes:v:30:y:2012:i:2:p:326-336 is not listed on IDEAS
  9. 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, December.
  10. Baumeister, Christiane & Kilian, Lutz, 2011. "Real-Time Forecasts of the Real Price of Oil," CEPR Discussion Papers 8414, C.E.P.R. Discussion Papers.
  11. Baumeister, Christiane & Kilian, Lutz, 2011. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," CEPR Discussion Papers 8698, C.E.P.R. Discussion Papers.
  12. 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.
  13. 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.
  14. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
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Cited by:
  1. Baumeister, Christiane & Kilian, Lutz & Zhou, Xiaoqing, 2013. "Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis," CEPR Discussion Papers 9572, C.E.P.R. Discussion Papers.

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