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Oil and US GDP: A real-time out-of-sample examination

Author

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  • Francesco Ravazzolo

    (Norges Bank (Central Bank of Norway))

  • Philip Rothman

    (East Carolina University)

Abstract

We study the real-time Granger-causal relationship between crude oil prices and US GDP growth through a simulated out-of-sample (OOS) forecasting exercise; we also provide strong evidence of in-sample predictability from oil prices to GDP. Comparing our benchmark model "without oil" against alternatives "with oil," we strongly reject the null hypothesis of no OOS predictability from oil prices to GDP via our point forecast comparisons from the mid-1980s through the Great Recession. Further analysis shows that these results may be due to our oil price measures serving as proxies for a recently developed measure of global real economic activity omitted from the alternatives to the benchmark forecasting models in which we only use lags of GDP growth. By way of density forecast OOS comparisons, we find evidence of such oil price predictability for GDP for our full 1970-2009 OOS period. Examination of the density forecasts reveals a massive increase in forecast uncertainty following the 1973 post-Yom Kippur War crude oil price increases.

Suggested Citation

  • Francesco Ravazzolo & Philip Rothman, 2010. "Oil and US GDP: A real-time out-of-sample examination," Working Paper 2010/18, Norges Bank.
  • Handle: RePEc:bno:worpap:2010_18
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    Cited by:

    1. Fan, Qinbin & Jahan-Parvar, Mohammad R., 2012. "U.S. industry-level returns and oil prices," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 112-128.
    2. Aramonte, Sirio & Jahan-Parvar, Mohammad R. & Shugarman, Justin K., 2019. "Institutions and return predictability in oil-exporting countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 14-26.
    3. Lutz Kilian & Robert J. Vigfusson, 2017. "The Role of Oil Price Shocks in Causing U.S. Recessions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(8), pages 1747-1776, December.
    4. Athanasios Triantafyllou & Dimitrios Bakas & Marilou Ioakimidis, 2019. "Commodity Price Uncertainty as a Leading Indicator of Economic Activity," Working Paper series 19-03, Rimini Centre for Economic Analysis.
    5. Lise Pichette & Marie-Noëlle Robitaille, 2017. "Assessing the Business Outlook Survey Indicator Using Real-Time Data," Discussion Papers 17-5, Bank of Canada.
    6. Claudio Morana, 2013. "The Oil Price-Macroeconomy Relationship Since the Mid-1980s: A Global Perspective," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    7. Raza, Syed Ali & Shahbaz, Muhammad & Amir-ud-Din, Rafi & Sbia, Rashid & Shah, Nida, 2018. "Testing for wavelet based time-frequency relationship between oil prices and US economic activity," Energy, Elsevier, vol. 154(C), pages 571-580.
    8. Lutz Kilian & Robert J. Vigfusson, 2013. "Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 78-93, January.
    9. John M. Maheu & Yong Song & Qiao Yang, 2018. "Oil Price Shocks and Economic Growth: The Volatility Link," Working Paper series 18-03, Rimini Centre for Economic Analysis.
    10. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    11. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, Elsevier.
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    14. Hamilton, James D., 2011. "Nonlinearities And The Macroeconomic Effects Of Oil Prices," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 364-378, November.
    15. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 326-336, September.
    16. Granziera, Eleonora & Hubrich, Kirstin & Moon, Hyungsik Roger, 2014. "A predictability test for a small number of nested models," Journal of Econometrics, Elsevier, vol. 182(1), pages 174-185.
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    19. Gaye GENCER & Sercan DEMIRALAY, 2013. "The impact of oil prices on sectoral returns: an empirical analysis from Borsa Istanbul," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(12(589)), pages 7-24, December.
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    22. Florackis, Chris & Giorgioni, Gianluigi & Kostakis, Alexandros & Milas, Costas, 2014. "On stock market illiquidity and real-time GDP growth," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 210-229.
    23. Gürkaynak, Refet S. & Kisacikoglu, Burçin & Rossi, Barbara, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    24. Ravazzolo Francesco & Rothman Philip, 2016. "Oil-price density forecasts of US GDP," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 441-453, September.
    25. Shiyi Chen & Dengke Chen & Wolfgang K. Härdle, 2014. "The Influence of Oil Price Shocks on China’s Macroeconomy : A Perspective of International Trade," SFB 649 Discussion Papers SFB649DP2014-063, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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