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Oil and US GDP: A Real-Time out-of Sample Examination

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  • Francesco Ravazzolo
  • Philip Rothman

Abstract

We study the real-time predictive content of crude oil prices for US real GDP growth through a pseudo out-of-sample (OOS) forecasting exercise. Comparing our benchmark model ?withoutoil? against alternatives ?with oil,? we strongly reject the null hypothesis of no OOS population-level predictability from oil prices to GDP at the longer forecast horizon we consider. 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. This examination of the global OOS relative performance of the models we consider is robust to use of ex-post revised data. But when we focus on the forecasting models? local relative performance, we observe strong differences across use of real-time and ex-post revised data.

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  • Francesco Ravazzolo & Philip Rothman, 2011. "Oil and US GDP: A Real-Time out-of Sample Examination," Working Papers No 2/2011, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  • Handle: RePEc:bny:wpaper:0004
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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. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, Elsevier.
    3. Aramonte, Sirio & Jahan-Parvar, Mohammad & Shugarman, Justin, 2015. "Institutions and return predictability in oil-exporting countries," Finance and Economics Discussion Series 2015-14, Board of Governors of the Federal Reserve System (U.S.).
    4. 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).
    5. Hamilton, James D., 2011. "Nonlinearities And The Macroeconomic Effects Of Oil Prices," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 364-378, November.
    6. 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.
    7. 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.
    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. 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.
    10. 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.
    11. Lise Pichette & Marie-Noëlle Robitaille, 2017. "Assessing the Business Outlook Survey Indicator Using Real-Time Data," Discussion Papers 17-5, Bank of Canada.
    12. 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.
    13. 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.
    14. 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.
    15. James D. Hamilton, 2012. "Oil Prices, Exhaustible Resources, and Economic Growth," NBER Working Papers 17759, National Bureau of Economic Research, Inc.
    16. 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.
    17. 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.
    18. Liu, Li & Ma, Feng & Wang, Yudong, 2015. "Forecasting excess stock returns with crude oil market data," Energy Economics, Elsevier, vol. 48(C), pages 316-324.
    19. 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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