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Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Deman

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  • Baumeister, Christiane
  • Hamilton, James

Abstract

Traditional approaches to structural vector autoregressions can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. We use this approach to revisit the importance of shocks to oil supply and demand. Supply disruptions turn out to be a bigger factor in historical oil price movements and inventory accumulation a smaller factor than implied by earlier estimates. Supply shocks lead to a reduction in global economic activity after a significant lag, whereas shocks to oil demand do not.

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  • Baumeister, Christiane & Hamilton, James, 2017. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Deman," CEPR Discussion Papers 12532, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:12532
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    Cited by:

    1. Blazsek, Szabolcs & Licht, Adrian, 2018. "Seasonal Quasi-Vector Autoregressive Models with an Application to Crude Oil Production and Economic Activity in the United States and Canada," UC3M Working papers. Economics 27484, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Duygu Ekin Ayasli & Yeliz Yalcin & Serkan Sahin & M. Hakan Berument, 2023. "Turkish Straits and an Important Oil Price Benchmark: Urals," The Energy Journal, , vol. 44(4), pages 277-300, July.
    3. Francesco Ravazzolo & Joaquin Vespignani, 2020. "World steel production: A new monthly indicator of global real economic activity," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(2), pages 743-766, May.
    4. Daniele Valenti, 2018. "Modelling the Global Price of Oil: Is there any Role for the Oil Futures-spot Spread?," Working Papers 2018.06, Fondazione Eni Enrico Mattei.
    5. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2018. "Formation of Market Beliefs in the Oil Market," CERGE-EI Working Papers wp619, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    6. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    7. 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.
    8. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    9. Lutz Kilian & Xiaoqing Zhou, 2018. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks: Comment," CESifo Working Paper Series 7166, CESifo.
    10. 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.
    11. 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.
    12. Punzi, Maria Teresa, 2019. "The impact of energy price uncertainty on macroeconomic variables," Energy Policy, Elsevier, vol. 129(C), pages 1306-1319.
    13. Blazsek, Szabolcs & Licht, Adrian, 2019. "Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production," UC3M Working papers. Economics 29030, Universidad Carlos III de Madrid. Departamento de Economía.
    14. repec:zbw:bofrdp:2018_014 is not listed on IDEAS
    15. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

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    More about this item

    Keywords

    Oil prices; Vector autoregressions; Sign restrictions; Measurement error; Bayesian inference;
    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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