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Refining the Workhorse Oil Market Model

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  • Xiaoqing Zhou

Abstract

The Kilian and Murphy (2014) structural vector autoregressive model has become the workhorse model for the analysis of oil markets. I explore various refinements and extensions of this model, including the effects of (1) correcting an error in the measure of global real economic activity, (2) explicitly incorporating narrative sign restrictions into the estimation, (3) relaxing the upper bound on the impact price elasticity of oil supply, (4) evaluating the implied posterior distribution of the structural models, and (5) extending the sample. I demonstrate that the substantive conclusions of Kilian and Murphy (2014) are largely unaffected by these changes.

Suggested Citation

  • Xiaoqing Zhou, 2019. "Refining the Workhorse Oil Market Model," Working Papers 1910, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddwp:1910
    DOI: 10.24149/wp1910
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    References listed on IDEAS

    as
    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. Bassam Fattouh, Lutz Kilian, and Lavan Mahadeva, 2013. "The Role of Speculation in Oil Markets: What Have We Learned So Far?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    3. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    4. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2018. "Narrative Sign Restrictions for SVARs," American Economic Review, American Economic Association, vol. 108(10), pages 2802-2829, October.
    5. Richard G. Newell and Brian C. Prest, 2019. "The Unconventional Oil Supply Boom: Aggregate Price Response from Microdata," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    6. Lutz Kilian, 2017. "The Impact of the Fracking Boom on Arab Oil Producers," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    7. Juan Antolin-Diaz & Juan F. Rubio-Ramirez, 2016. "Narrative Sign Restrictions for SVARs," FRB Atlanta Working Paper 2016-16, Federal Reserve Bank of Atlanta.
    8. 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.
    9. Soren T. Anderson & Ryan Kellogg & Stephen W. Salant, 2018. "Hotelling under Pressure," Journal of Political Economy, University of Chicago Press, vol. 126(3), pages 984-1026.
    10. James D. Hamilton, 2019. "Measuring Global Economic Activity," NBER Working Papers 25778, National Bureau of Economic Research, Inc.
    11. 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.
    12. 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.
    13. Kilian, Lutz, 2019. "Measuring global real economic activity: Do recent critiques hold up to scrutiny?," Economics Letters, Elsevier, vol. 178(C), pages 106-110.
    14. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Oil market; global real activity; structural VAR; narrative sign restrictions; identification; Bayesian inference;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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