ADVANCES IN USING VECTOR AUTOREGRESSIONS TO ESTIMATE STRUCTURAL MAGNITUDES

This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), 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.

Automated summary

This paper surveys recent advances in drawing structural conclusions from VARs and proposes the use of Bayesian inference to account for imperfect or erroneous prior information. It raises concerns about the reporting of VARs results that are set-identified using sign and other restrictions, and shows how to correctly estimate structural elasticities even with limited knowledge of shock effects.