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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. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Working Papers 11-16, Bank of Canada.
  2. Ron Alquist & Lutz Kilian & Robert J. Vigfusson, 2011. "Forecasting the Price of Oil," Working Papers 11-15, Bank of Canada.
  3. 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.
  4. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  5. Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.
  6. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  7. 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.
  8. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
  9. Giannone, Domenico & Henry, Jérôme & Lalik, Magdalena & Modugno, Michele, 2010. "An area-wide real-time database for the euro area," Working Paper Series 1145, European Central Bank.
  10. Morten O. Ravn & Harald Uhlig, 2002. "On adjusting the Hodrick-Prescott filter for the frequency of observations," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 371-375.
  11. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2012. "Prior selection for vector autoregressions," Working Paper Series 1494, European Central Bank.
  12. D'Agostino, Antonello & Gambetti, Luca & Giannone, Domenico & Giannone, Domenico, 2009. "Macroeconomic Forecasting and Structural Change," Research Technical Papers 8/RT/09, Central Bank of Ireland.
  13. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 0214, European Central Bank.
  14. Cogley, Timothy & Morozov, Sergei & Sargent, Thomas J., 2005. "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1893-1925, November.
  15. 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.
  16. 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.
  17. 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.
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Citations

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Cited by:
  1. 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).
  2. Chen, Shiu-Sheng, 2013. "Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks," MPRA Paper 49240, University Library of Munich, Germany.
  3. 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.
  4. Baumeister, Christiane & Kilian, Lutz, 2013. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," CEPR Discussion Papers 9569, C.E.P.R. Discussion Papers.

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