Working Paper: NBER ID: w24167
Authors: Christiane JS Baumeister; James D Hamilton
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: structural vector autoregressions; oil supply shocks; oil demand shocks; Bayesian inference
JEL Codes: C32; E32; Q43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| oil supply shocks (Q43) | global economic activity (F69) |
| oil demand shocks (Q43) | global economic activity (F69) |
| oil demand shocks (Q43) | oil prices (L71) |
| weak demand and strong supply (J20) | oil price collapse (2014-2016) (L71) |
| stronger demand (R22) | rebound in oil prices (2016) (Q31) |