Abstract:
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.
Description:
This paper was presented as the Econometric Theory Lecture at the EC2 Conference on The Econometrics
of Climate, Energy and Resources at CREATES in December 2021. The paper supersedes earlier papers
by the authors that were circulated under the titles "Advances in Structural Vector Autoregressions with
Imperfect Identifying Information" and "Estimating Structural Parameters Using Vector Autoregressions".
