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Structural Interpretation of Vector Autoregressions with Incomplete Information: Revisiting the Role of Oil Supply and Demand S

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  • Kilian, Lutz
  • Zhou, Xiaoqing

Abstract

Recently, Baumeister and Hamilton (henceforth: BH) have argued that existing studies of the global oil market fail to account for uncertainty about their identifying assumptions. They recommend an alternative econometric approach intended to address this concern by formulating priors on the structural model parameters. We demonstrate that in practice BH are unable to parameterize identification uncertainty without falling back on ad hoc prior specifications. They are also unable to show that earlier studies did not impose all relevant identifying information. In fact, to the extent that BH’s substantive conclusions differ from earlier studies, these differences do not reflect their use of a superior econometric methodology, but mainly the imposition of a highly unrealistic prior for the global impact price elasticity of oil supply. Once identification uncertainty about the global price elasticity of oil supply is accounted for by specifying a prior more in line with extraneous evidence and economic theory, the substantive results of earlier oil market studies are reaffirmed. We also refute BH’s claim that existing oil market studies are invalid or not robust. Finally, we explain why the BH method is not a generalization of all existing methods. It is, in fact, not designed to be applied to state-or-the-art oil market models because key assumptions of the proposed approach are not met in these models.

Suggested Citation

  • Kilian, Lutz & Zhou, Xiaoqing, 2018. "Structural Interpretation of Vector Autoregressions with Incomplete Information: Revisiting the Role of Oil Supply and Demand S," CEPR Discussion Papers 13068, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:13068
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    References listed on IDEAS

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    1. 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.
    2. John Coglianese & Lucas W. Davis & Lutz Kilian & James H. Stock, 2017. "Anticipation, Tax Avoidance, and the Price Elasticity of Gasoline Demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 1-15, January.
    3. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    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, 2008. "The Economic Effects of Energy Price Shocks," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 871-909, December.
    7. Hamilton, James D., 2011. "Nonlinearities And The Macroeconomic Effects Of Oil Prices," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 364-378, November.
    8. Lutz Kilian, 2017. "The Impact of the Fracking Boom on Arab Oil Producers," The Energy Journal, , vol. 38(6), pages 137-160, November.
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    11. 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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    13. Richard G. Newell & Brian C. Prest & Ashley Vissing, 2016. "Trophy Hunting vs. Manufacturing Energy: The Price-Responsiveness of Shale Gas," NBER Working Papers 22532, National Bureau of Economic Research, Inc.
    14. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Modeling fluctuations in the global demand for commodities," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 54-78.
    15. 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.
    16. Hilde C. Bjørnland & Frode Martin Nordvik & Maximilian Rohrer, 2021. "Supply flexibility in the shale patch: Evidence from North Dakota," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 273-292, April.
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    18. Lutz Kilian, 2008. "Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 216-240, May.
    19. 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.
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    Cited by:

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    3. Bilgin, Doga & Ellwanger, Reinhard, 2024. "A simple model of global fuel consumption," Energy Economics, Elsevier, vol. 130(C).
    4. Arampatzidis, Ioannis & Panagiotidis, Theodore, 2023. "On the identification of the oil-stock market relationship," Economic Modelling, Elsevier, vol. 120(C).
    5. Peng Li & Yaofu Ouyang, 2023. "Oil price shocks and China’s consumer and entrepreneur sentiment: a Bayesian structural VAR approach," Empirical Economics, Springer, vol. 65(5), pages 2241-2271, November.
    6. Hau, Liya & Zhu, Huiming & Huang, Rui & Ma, Xiang, 2020. "Heterogeneous dependence between crude oil price volatility and China’s agriculture commodity futures: Evidence from quantile-on-quantile regression," Energy, Elsevier, vol. 213(C).

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    More about this item

    Keywords

    Oil market models; Structural var. identification; Oil supply elasticity;
    All these keywords.

    JEL classification:

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

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