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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.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:jnlbes:v:31:y:2013:i:1:p:78-93 DOI: 10.1080/07350015.2012.740436
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    References listed on IDEAS

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    1. Hamilton, James D., 1996. "This is what happened to the oil price-macroeconomy relationship," Journal of Monetary Economics, Elsevier, vol. 38(2), pages 215-220, October.
    2. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, pages 160-177.
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    Cited by:

    1. Shiu-Sheng Chen, 2014. "Forecasting Crude Oil Price Movements With Oil-Sensitive Stocks," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 830-844, April.
    2. Piergiorgio Alessandri & Antonio M. Conti & Fabrizio Venditti, 2016. "The Financial Stability Dark Side of Monetary Policy," BCAM Working Papers 1601, Birkbeck Centre for Applied Macroeconomics.
    3. Maheu, John M & Yang, Qiao & Song, Yong, 2018. "Oil Price Shocks and Economic Growth: The Volatility Link," MPRA Paper 83779, University Library of Munich, Germany.
    4. repec:eee:eneeco:v:64:y:2017:i:c:p:298-305 is not listed on IDEAS
    5. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, vol. 61(C), pages 72-86.
    6. 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.
    7. Jacint Balaguer & Manuel Cantavella-Jordá, 2014. "The Effect of Economic Growth and Oil Price Variations on CO2 Emissions: Evidence from Spain (1874-2011)," Working Papers 2014/22, Economics Department, Universitat Jaume I, Castellón (Spain).
    8. repec:eee:eneeco:v:66:y:2017:i:c:p:337-348 is not listed on IDEAS
    9. An, Lian & Jin, Xiaoze & Ren, Xiaomei, 2014. "Are the macroeconomic effects of oil price shock symmetric?: A Factor-Augmented Vector Autoregressive approach," Energy Economics, Elsevier, vol. 45(C), pages 217-228.
    10. Chen, Hongtao & Liu, Li & Wang, Yudong & Zhu, Yingming, 2016. "Oil price shocks and U.S. dollar exchange rates," Energy, Elsevier, vol. 112(C), pages 1036-1048.
    11. Riggi, Marianna & Venditti, Fabrizio, 2015. "The time varying effect of oil price shocks on euro-area exports," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 75-94.
    12. repec:eee:jjieco:v:46:y:2017:i:c:p:1-16 is not listed on IDEAS
    13. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, pages 181-186.
    14. repec:ebl:ecbull:eb-17-00609 is not listed on IDEAS
    15. Pal, Debdatta & Mitra, Subrata Kumar, 2015. "Asymmetric impact of crude price on oil product pricing in the United States: An application of multiple threshold nonlinear autoregressive distributed lag model," Economic Modelling, Elsevier, vol. 51(C), pages 436-443.
    16. Shah, Imran H. & Carlos, Diaz Vela & Wang, Yuan, 2017. "Revisiting the Dynamics Effects of Oil Price Shocks on Small Developing Economies," Department of Economics Working Papers 58122, University of Bath, Department of Economics.
    17. Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2016. "Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data," Working Papers 201685, University of Pretoria, Department of Economics.
    18. Roach, Travis, 2015. "Hidden regimes and the demand for carbon dioxide from motor-gasoline," Energy Economics, Elsevier, vol. 52(PB), pages 306-315.
    19. 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.
    20. Fresoli, Diego & Ruiz, Esther & Pascual, Lorenzo, 2015. "Bootstrap multi-step forecasts of non-Gaussian VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 834-848.
    21. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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