Working Paper: NBER ID: w27014
Authors: Christiane Baumeister; James D. Hamilton
Abstract: This paper surveys recent advances in drawing structural conclusions from vector autoregressions, providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.
Keywords: Vector Autoregressions; Bayesian Inference; Structural Elasticities
JEL Codes: C11; C32; Q43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| exact prior information (D89) | accurate estimation of structural magnitudes (C51) |
| inexact prior information (D89) | inconsistent estimates (C51) |
| single structural shock (C69) | erroneous conclusions (Y50) |
| increase in oil prices (Q31) | quantity demanded (E41) |
| increase in oil prices (Q31) | economic activity (E20) |
| Bayesian approach (C11) | enhanced robustness of structural estimates (C51) |