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What Central Bankers Need to Know about Forecasting Oil Prices

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  • Christiane Baumeister
  • Lutz Kilian

Abstract

Forecasts of the quarterly real price of oil are routinely used by international organizations and central banks worldwide in assessing the global and domestic economic outlook, yet little is known about how best to generate such forecasts. Our analysis breaks new ground in several dimensions. First, we address a number of econometric and data issues specific to real-time forecasts of quarterly oil prices. Second, we develop real-time forecasting models not only for U.S. benchmarks such as West Texas Intermediate crude oil, but we also develop forecasting models for the price of Brent crude oil, which has become increasingly accepted as the best measure of the global price of oil in recent years. Third, we design for the first time methods for forecasting the real price of oil in foreign consumption units rather than U.S. consumption units, taking the point of view of forecasters outside the United States. In addition, we investigate the costs and benefits of allowing for time variation in vector autoregressive (VAR) model parameters and of constructing forecast combinations. We conclude that quarterly forecasts of the real price of oil from suitably designed VAR models estimated on monthly data generate the most accurate forecasts among a wide range of methods including forecasts based on oil futures prices, no-change forecasts and forecasts based on regression models estimated on quarterly data.

Suggested Citation

  • Christiane Baumeister & Lutz Kilian, 2013. "What Central Bankers Need to Know about Forecasting Oil Prices," Staff Working Papers 13-15, Bank of Canada.
  • Handle: RePEc:bca:bocawp:13-15
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Shiu-Sheng Chen, 2014. "Forecasting Crude Oil Price Movements With Oil-Sensitive Stocks," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 830-844, April.
    2. 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.
    3. Panopoulou, Ekaterini & Pantelidis, Theologos, 2015. "Speculative behaviour and oil price predictability," Economic Modelling, Elsevier, vol. 47(C), pages 128-136.
    4. 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.
    5. 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.
    6. Reinhard Ellwanger, Stephen Snudden, 2021. "Predictability of Aggregated Time Series," LCERPA Working Papers bm0127, Laurier Centre for Economic Research and Policy Analysis.
    7. Samya Beidas-Strom & Benjamin Beckers, 2015. "Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All?," IMF Working Papers 2015/251, International Monetary Fund.
    8. Frankel, Jeffrey A., 2014. "Effects of speculation and interest rates in a “carry trade” model of commodity prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 88-112.

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    More about this item

    Keywords

    Econometric and statistical methods; International topics;

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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