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Are there Gains from Pooling Real-Time Oil Price Forecasts?

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Listed:
  • Baumeister, Christiane
  • Kilian, Lutz
  • Lee, Thomas K

Abstract

The answer depends on the objective. The approach of combining five of the leading forecasting models with equal weights dominates the strategy of selecting one model and using it for all horizons up to two years. Even more accurate forecasts, however, are obtained when allowing the forecast combinations to vary across forecast horizons. While the latter approach is not always more accurate than selecting the single most accurate forecasting model by horizon, its accuracy can be shown to be much more stable over time. The MSPE of real-time pooled forecasts is between 3% and 29% lower than that of the no-change forecast and its directional accuracy as high as 73%. Our results are robust to alternative oil price measures and apply to monthly as well as quarterly forecasts. We illustrate how forecast pooling may be used to produce real-time forecasts of the real and the nominal price of oil in a format consistent with that employed by the U.S. Energy Information Administration in releasing its short-term oil price forecasts, and we compare these forecasts during key historical episodes.

Suggested Citation

  • Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K, 2014. "Are there Gains from Pooling Real-Time Oil Price Forecasts?," CEPR Discussion Papers 10075, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:10075
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    1. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
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    4. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
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    6. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    7. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55, pages 869-889, August.
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    More about this item

    Keywords

    forecast combination; forecast pooling; oil price; real-time data; refiners' acquisition cost; WTI;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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