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Real-Time Forecasts of the Real Price of Oil

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  • Christiane Baumeister
  • Lutz Kilian

Abstract

We construct a monthly real-time data set consisting of vintages for 1991.1-2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques to fill gaps in the real-time data. We show that real-time forecasts of the real price of oil can be more accurate than the no-change forecast at horizons up to one year. In some cases real-time MSPE reductions may be as high as 25 percent one month ahead and 24 percent three months ahead. This result is in striking contrast to related results in the literature for asset prices. In particular, recursive vector autoregressive (VAR) forecasts based on global oil market variables tend to have lower MSPE at short horizons than forecasts based on oil futures prices, forecasts based on AR and ARMA models, and the no-change forecast. In addition, these VAR models have consistently higher directional accuracy. We demonstrate how with additional identifying assumptions such VAR models may be used not only to understand historical fluctuations in the real price of oil, but to construct conditional forecasts that reflect hypothetical scenarios about future demand and supply conditions in the market for crude oil. These tools are designed to allow forecasters to interpret their oil price forecast in light of economic models and to evaluate its sensitivity to alternative assumptions.

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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 11-16.

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Length: 47 pages
Date of creation: 2011
Date of revision:
Handle: RePEc:bca:bocawp:11-16

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Keywords: Econometric and statistical methods; International topics;

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References

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  1. Lutz Kilian & Clara Vega, 2008. "Do energy prices respond to U.S. macroeconomic news? a test of the hypothesis of predetermined energy prices," International Finance Discussion Papers 957, Board of Governors of the Federal Reserve System (U.S.).
  2. 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.
  3. Giannone, Domenico & Reichlin, Lucrezia, 2006. "Does information help recovering structural shocks from past observations?," Working Paper Series 0632, European Central Bank.
  4. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
  5. C. Baumeister & G. Peersman & -, 2010. "Sources of the Volatility Puzzle in the Crude Oil Market," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/634, Ghent University, Faculty of Economics and Business Administration.
  6. 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.
  7. Evan Koenig & Sheila Dolmas & Jeremy M. Piger, 2002. "The use and abuse of 'real-time' data in economic forecasting," Working Papers 2001-015, Federal Reserve Bank of St. Louis.
  8. 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.
  9. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," NBER Working Papers 18467, National Bureau of Economic Research, Inc.
  10. 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.
  11. Domenico Giannone & Lucrezia Reichlin, 2006. "Does information help recovering structural shocks from past observations?," ULB Institutional Repository 2013/10125, ULB -- Universite Libre de Bruxelles.
  12. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
  13. Daniel F. Waggoner & Tao Zha, 1998. "Conditional forecasts in dynamic multivariate models," Working Paper 98-22, Federal Reserve Bank of Atlanta.
  14. Kilian, Lutz, 2005. "Exogenous Oil Supply Shocks: How Big Are They and How Much do they Matter for the US Economy?," CEPR Discussion Papers 5131, C.E.P.R. Discussion Papers.
  15. 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.
  16. Francesco Ravazzolo & Philip Rothman, 2010. "Oil and US GDP: A real-time out-of-sample examination," Working Paper 2010/18, Norges Bank.
  17. Hicks, Bruce & Kilian, Lutz, 2009. "Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003-2008?," CEPR Discussion Papers 7265, C.E.P.R. Discussion Papers.
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