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Long-Term Oil Price Forecasts: A New Perspective On Oil And The Macroeconomy

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  • Miller, J. Isaac
  • Ni, Shawn

Abstract

We examine how future real GDP growth relates to changes in the forecasted long-term average of discounted real oil prices and to changes in unanticipated fluctuations of real oil prices around the forecasts. Forecasts are conducted using a state-space oil market model, in which global real economic activity and real oil prices share a common stochastic trend. Changes in unanticipated fluctuations and changes in the forecasted long-term average of discounted real oil prices sum to real oil price changes. We find that these two components have distinctly different relationships with future real GDP growth. Positive and negative changes in the unanticipated fluctuations of real oil prices correlate with asymmetric responses of future real GDP growth. In comparison, changes in the forecasted long-term average are smaller in magnitude but are more influential on real GDP. Persistent upward revisions of forecasts in the 2000s had a substantial negative impact on real GDP growth, according to our estimates.

Suggested Citation

  • Miller, J. Isaac & Ni, Shawn, 2011. "Long-Term Oil Price Forecasts: A New Perspective On Oil And The Macroeconomy," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 396-415, November.
  • Handle: RePEc:cup:macdyn:v:15:y:2011:i:s3:p:396-415_00
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    Cited by:

    1. 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).
    2. Turhan, M. Ibrahim & Sensoy, Ahmet & Ozturk, Kevser & Hacihasanoglu, Erk, 2014. "A view to the long-run dynamic relationship between crude oil and the major asset classes," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 286-299.
    3. Ratti, Ronald A. & Vespignani, Joaquin L., 2013. "Liquidity and crude oil prices: China's influence over 1996–2011," Economic Modelling, Elsevier, vol. 33(C), pages 517-525.
    4. Ratti, Ronald A. & Vespignani, Joaquin L., 2012. "Liquidity and Crude Oil Prices: China’s Influence Over 1996-2011," MPRA Paper 48900, University Library of Munich, Germany.
    5. Zhang, Chuanguo & Chen, Xiaoqing, 2014. "The impact of global oil price shocks on China’s bulk commodity markets and fundamental industries," Energy Policy, Elsevier, vol. 66(C), pages 32-41.
    6. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(3), pages 584-614.
    7. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 1-20, March.
    8. Alexander HARIN, 2014. "Partially Unforeseen Events. Corrections and Correcting Formulae for Forecasts," Expert Journal of Economics, Sprint Investify, vol. 2(2), pages 69-79.

    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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