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Facts and Fiction in Oil Market Modeling

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  • Lutz Kilian

Abstract

A series of recent articles has called into question the validity of VAR models of the global market for crude oil. These studies seek to replace existing oil market models by structural VAR models of their own based on different data, different identifying assumptions, and a different econometric approach. Their main aim has been to revise the consensus in the literature that oil demand shocks are a more important determinant of oil price fluctuations than oil supply shocks. Substantial progress has been made in recent years in sorting out the pros and cons of the underlying econometric methodologies and data in this debate, and in separating claims that are supported by empirical evidence from claims that are not. The purpose of this paper is to take stock of the VAR literature on global oil markets and to synthesize what we have learned. Combining this evidence with new data and analysis, I make the case that the concerns regarding the existing VAR oil market literature have been overstated and that the results from these models are quite robust to changes in the model specification.

Suggested Citation

  • Lutz Kilian, 2019. "Facts and Fiction in Oil Market Modeling," Working Papers 1907, Federal Reserve Bank of Dallas, revised 21 Dec 2020.
  • Handle: RePEc:fip:feddwp:1907
    DOI: 10.24149/wp1907r1
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    1. Kilian, Lutz, 2019. "Measuring global real economic activity: Do recent critiques hold up to scrutiny?," Economics Letters, Elsevier, vol. 178(C), pages 106-110.
    2. 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).
    3. 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.
    4. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    5. Xiaoqing Zhou, 2019. "Refining the Workhorse Oil Market Model," Working Papers 1910, Federal Reserve Bank of Dallas.
    6. Delle Chiaie, Simona & Ferrara, Laurent & Giannone, Domenico, 2018. "Common factors of commodity prices," Research Bulletin, European Central Bank, vol. 51.
    7. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    8. Lutz Kilian & Xiaoqing Zhou, 2019. "Oil prices, exchange rates, and interest rates," 2019 Meeting Papers 592, Society for Economic Dynamics.
    9. 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.
    10. Anthony Garratt & Shaun P. Vahey & Yunyi Zhang, 2019. "Real‐time forecast combinations for the oil price," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 456-462, April.
    11. Caner, M. & Kilian, L., 2001. "Size distortions of tests of the null hypothesis of stationarity: evidence and implications for the PPP debate," Journal of International Money and Finance, Elsevier, vol. 20(5), pages 639-657, October.
    12. 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.
    13. 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.
    14. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 665-696.
    15. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    16. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, October.
    17. Lutz Kilian & Xiaoqing Zhou, 2020. "Does drawing down the US Strategic Petroleum Reserve help stabilize oil prices?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 673-691, September.
    18. Maurizio Michael Habib & Sascha Bützer & Livio Stracca, 2016. "Global Exchange Rate Configurations: Do Oil Shocks Matter?," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 64(3), pages 443-470, August.
    19. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    20. Waggoner, Daniel F. & Zha, Tao, 2012. "Confronting model misspecification in macroeconomics," Journal of Econometrics, Elsevier, vol. 171(2), pages 167-184.
    21. 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).
    22. Caldara, Dario & Cavallo, Michele & Iacoviello, Matteo, 2019. "Oil price elasticities and oil price fluctuations," Journal of Monetary Economics, Elsevier, vol. 103(C), pages 1-20.
    23. Christopher A. Sims & Tao Zha, 1999. "Error Bands for Impulse Responses," Econometrica, Econometric Society, vol. 67(5), pages 1113-1156, September.
    24. 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.
    25. 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.
    26. Koop, Gary, 1996. "Parameter uncertainty and impulse response analysis," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 135-149.
    27. 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.
    28. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    29. Lutz Kilian, 2008. "The Economic Effects of Energy Price Shocks," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 871-909, December.
    30. James D. Hamilton, 2019. "Measuring Global Economic Activity," NBER Working Papers 25778, National Bureau of Economic Research, Inc.
    31. 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.
    32. 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.
    33. Lutz Kilian, 1999. "Finite-Sample Properties of Percentile and Percentile-t Bootstrap Confidence Intervals for Impulse Responses," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 652-660, November.
    34. 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.
    35. Regli, Frederik & Nomikos, Nikos K., 2019. "The eye in the sky – Freight rate effects of tanker supply," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 402-424.
    36. Juan Antolin-Diaz & Juan F. Rubio-Ramirez, 2016. "Narrative Sign Restrictions for SVARs," FRB Atlanta Working Paper 2016-16, Federal Reserve Bank of Atlanta.
    37. Martin Bruns & Michele Piffer, 2018. "Bayesian Structural VAR models: a new approach for prior beliefs on impulse responses," Working Papers 878, Queen Mary University of London, School of Economics and Finance.
    38. Zha, Tao, 1999. "Block recursion and structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 90(2), pages 291-316, June.
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    Cited by:

    1. Nonejad, Nima, 2020. "An observation regarding Hamilton’s recent criticisms of Kilian’s global real economic activity index," Economics Letters, Elsevier, vol. 196(C).
    2. Palmén, Olli, 2020. "Sovereign default risk and credit supply: Evidence from the euro area," Journal of International Money and Finance, Elsevier, vol. 109(C).
    3. Zeina Alsalman, 0. "Does the source of oil supply shock matter in explaining the behavior of U.S. consumer spending and sentiment?," Empirical Economics, Springer, vol. 0, pages 1-28.
    4. Hilde C. Bjørnland, 2019. "Supply flexibility in the shale patch: Facts, no fiction," Working Papers No 08/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    5. Dalheimer, Bernhard & Herwartz, Helmut & Lange, Alexander, 2021. "The threat of oil market turmoils to food price stability in Sub-Saharan Africa," Energy Economics, Elsevier, vol. 93(C).

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    More about this item

    Keywords

    Elasticity; structural VAR; Bayesian inference; oil price; global real activity; oil inventories;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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