A0481
Title: Advances in using vector autoregressions to estimate structural magnitudes
Authors: Christiane Baumeister - University of Notre Dame (United States) [presenting]
Abstract: Recent advances are surveyed for 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 signs 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.
