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

Christiane Baumeister and James D. Hamilton

No 24167, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C32 E32 Q43 (search for similar items in EconPapers)
Date: 2017-12
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-ets and nep-mac
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Citations: View citations in EconPapers (17)

Published as Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, vol 109(5), pages 1873-1910.

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Journal Article: Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks (2019)
Working Paper: Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks (2017)
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