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What Central Bankers Need to Know about Forecasting Oil Prices

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  • Baumeister, Christiane
  • Kilian, Lutz

Abstract

Recent research has shown that recursive real-time VAR forecasts of the real price of oil tend to be more accurate than forecasts based on oil futures prices of the type commonly employed by central banks worldwide. Such monthly forecasts, however, differ in several important dimensions from the forecasts central banks require when making policy decisions. First, central banks are interested in forecasts of the quarterly real price of oil rather than forecasts of the monthly real price of oil. Second, many central banks are interested in forecasting the real price of Brent crude oil rather than any of the U.S. benchmarks. Third, central banks outside the United States are interested in forecasting the real price of oil measured in domestic consumption units rather than U.S. consumption units. Addressing each of these three concerns involves modeling choices that affect the relative accuracy of alternative forecasting methods. In addition, we investigate the costs and benefits of allowing for time variation in 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, nochange forecasts and forecasts based on models estimated on quarterly data.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 9118.

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Date of creation: Sep 2012
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Handle: RePEc:cpr:ceprdp:9118

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Keywords: Central banks; Forecasting methods; Oil futures prices; Out-of-sample forecast; Quarterly horizon; Real price of oil; Real-time data; VAR;

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References

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  1. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2009. "Macroeconomic Forecasting and Structural Change," Working Papers ECARES 2009_020, ULB -- Universite Libre de Bruxelles.
  2. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michèle Modugno, 2010. "An Area Wide Real Time Data Base for the Euro Area," Working Papers ECARES ECARES 2010-026, ULB -- Universite Libre de Bruxelles.
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  5. Todd E. Clark & Michael W. McCracken, 2008. "Tests of equal predictive ability with real-time data," Working Papers 2008-029, Federal Reserve Bank of St. Louis.
  6. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 0214, European Central Bank.
  7. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Working Papers 11-16, Bank of Canada.
  8. Christiane Baumeister & Gert Peersman, 2011. "The Role of Time-Varying Price Elasticities in Accounting for Volatility Changes in the Crude Oil Market," Working Papers 11-28, Bank of Canada.
  9. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, 04.
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  12. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E, 2012. "Prior Selection for Vector Autoregressions," CEPR Discussion Papers 8755, 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. Ron Alquist & Lutz Kilian & Robert J. Vigfusson, 2011. "Forecasting the price of oil," International Finance Discussion Papers 1022, Board of Governors of the Federal Reserve System (U.S.).
  15. 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.
  16. Cogley, Timothy W. & Morozov, Sergei & Sargent, Thomas J., 2003. "Bayesian fan charts for UK inflation: Forecasting and sources of uncertainty in an evolving monetary system," CFS Working Paper Series 2003/44, Center for Financial Studies (CFS).
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Cited by:
  1. Baumeister, Christiane & Kilian, Lutz, 2013. "Forecasting the real price of oil in a changing world: A forecast combination approach," CFS Working Paper Series 2013/11, Center for Financial Studies (CFS).
  2. 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).
  3. Christiane Baumeister & Pierre Guérin & Lutz Kilian, 2014. "Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work," Working Papers 14-11, Bank of Canada.
  4. Chen, Shiu-Sheng, 2013. "Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks," MPRA Paper 49240, University Library of Munich, Germany.
  5. Jeffrey A. Frankel, 2013. "Effects of Speculation and Interest Rates in a “Carry Trade” Model of Commodity Prices," NBER Working Papers 19463, National Bureau of Economic Research, Inc.

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