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

Christiane Baumeister () and James D. Hamilton

No 6835, CESifo Working Paper Series from CESifo Group Munich

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.

Keywords: oil prices; vector autoregressions; sign restrictions; Bayesian inference; measurement error (search for similar items in EconPapers)
JEL-codes: Q43 C32 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ene and nep-mac
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10) Track citations by RSS feed

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