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The macroeconomic effects of oil price shocks: Evidence from a statistical identification approach

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  • Herwartz, Helmut
  • Plödt, Martin

Abstract

We analyze the dynamics in the global crude oil market based on a structural vector autoregressive model. We identify the model by presuming that reduced form residuals can be traced back to structural shocks that are independently distributed over the cross equation dimension. The resulting point estimates of the impulse response functions allow for a direct comparison with the outcomes of more conventional identification approaches. Our results are remarkably similar to the results regarding oil market dynamics in Kilian and Murphy (2012) and Inoue and Kilian (2013) even though they rely on statistical arguments instead of a set of theory-based a priori restrictions. Based on the results from our statistical approach, we investigate the cumulative contributions of different oil shocks on the rapid fall in oil prices at the end of 2008 and 2014, as well as the effects of different oil shocks on macroeconomic aggregates in the US, the euro area, and China.

Suggested Citation

  • Herwartz, Helmut & Plödt, Martin, 2016. "The macroeconomic effects of oil price shocks: Evidence from a statistical identification approach," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 30-44.
  • Handle: RePEc:eee:jimfin:v:61:y:2016:i:c:p:30-44
    DOI: 10.1016/j.jimonfin.2015.11.001
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    References listed on IDEAS

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    1. Kilian, Lutz & Demiroglu, Ufuk, 2000. "Residual-Based Tests for Normality in Autoregressions: Asymptotic Theory and Simulation Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 40-50, January.
    2. Kilian, Lutz & Lee, Thomas K., 2014. "Quantifying the speculative component in the real price of oil: The role of global oil inventories," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 71-87.
    3. Christiane Baumeister & Gert Peersman, 2013. "The Role Of Time‐Varying Price Elasticities In Accounting For Volatility Changes In The Crude Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1087-1109, November.
    4. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    5. Renée Fry & Adrian Pagan, 2011. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 938-960, December.
    6. Lutz Kilian, 2016. "The Impact of the Shale Oil Revolution on U.S. Oil and Gasoline Prices," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 10(2), pages 185-205.
    7. Lutz Kilian & Logan T. Lewis, 2011. "Does the Fed Respond to Oil Price Shocks?," Economic Journal, Royal Economic Society, vol. 121(555), pages 1047-1072, September.
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    12. Francesco Lippi & Andrea Nobili, 2012. "Oil And The Macroeconomy: A Quantitative Structural Analysis," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1059-1083, October.
    13. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
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    16. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    17. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    18. Martin Bodenstein & Luca Guerrieri & Lutz Kilian, 2012. "Monetary Policy Responses to Oil Price Fluctuations," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(4), pages 470-504, December.
    19. Gulasekaran Rajaguru & Tilak Abeysinghe, 2004. "Quarterly real GDP estimates for China and ASEAN4 with a forecast evaluation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 431-447.
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    More about this item

    Keywords

    Vector autoregression; Identification; Global oil market;
    All these keywords.

    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
    • 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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