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Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries

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  • Lutz Kilian
  • Robert J. Vigfusson

Abstract

There is a long tradition of using oil prices to forecast U.S. real GDP. It has been suggested that the predictive relationship between the price of oil and one-quarter-ahead U.S. real GDP is nonlinear in that (a) oil price increases matter only to the extent that they exceed the maximum oil price in recent years, and that (b) oil price decreases do not matter at all. We examine, first, whether the evidence of in-sample predictability in support of this view extends to out-of-sample forecasts. Second, we discuss how to extend this forecasting approach to higher horizons. Third, we compare the resulting class of nonlinear models to alternative economically plausible nonlinear specifications and examine which aspect of the model is most useful for forecasting. We show that the asymmetry embodied in commonly used nonlinear transformations of the price of oil is not helpful for out-of-sample forecasting; more robust and often more accurate real GDP forecasts are obtained from symmetric nonlinear models based on the 3-year net oil price change. Finally, we quantify the extent to which the 2008 recession could have been forecast using the latter class of time-varying threshold models.

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File URL: http://hdl.handle.net/10.1080/07350015.2012.740436
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.

Volume (Year): 31 (2013)
Issue (Month): 1 (January)
Pages: 78-93

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Handle: RePEc:taf:jnlbes:v:31:y:2013:i:1:p:78-93

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References

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  1. Lucas W. Davis & Lutz Kilian, 2009. "Estimating the Effect of a Gasoline Tax on Carbon Emissions," NBER Working Papers 14685, National Bureau of Economic Research, Inc.
  2. Francesco Ravazzolo & Philip Rothman, 2010. "Oil and US GDP: A real-time out-of-sample examination," Working Paper 2010/18, Norges Bank.
  3. Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Working Papers 2009-051, Federal Reserve Bank of St. Louis.
  4. Lutz Kilian & Robert J. Vigfusson, 2011. "Are the responses of the U.S. economy asymmetric in energy price increases and decreases?," Quantitative Economics, Econometric Society, vol. 2(3), pages 419-453, November.
  5. James D. Hamilton, 2000. "What is an Oil Shock?," NBER Working Papers 7755, National Bureau of Economic Research, Inc.
  6. Todd E. Clark & Michael W. McCracken, 2009. "Nested forecast model comparisons: a new approach to testing equal accuracy," Research Working Paper RWP 09-11, Federal Reserve Bank of Kansas City.
  7. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
  8. Lutz Kilian & Robert J. Vigfusson, 2011. "Nonlinearities in the oil price-output relationship," International Finance Discussion Papers 1013, Board of Governors of the Federal Reserve System (U.S.).
  9. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  10. Herrera, Ana María & Lagalo, Latika Gupta & Wada, Tatsuma, 2011. "Oil Price Shocks And Industrial Production: Is The Relationship Linear?," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 472-497, November.
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Cited by:
  1. Chen, Shiu-Sheng, 2013. "Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks," MPRA Paper 49240, University Library of Munich, Germany.

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