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ADVANCES IN USING VECTOR AUTOREGRESSIONS TO ESTIMATE STRUCTURAL MAGNITUDES

Published online by Cambridge University Press:  07 November 2022

Christiane Baumeister Open the ORCID record for Christiane Baumeister [Opens in a new window]  and
James D. Hamilton Open the ORCID record for James D. Hamilton [Opens in a new window]
Christiane Baumeister*
Affiliation:
University of Notre Dame and University of Pretoria
James D. Hamilton
Affiliation:
University of California at San Diego
*
Address correspondence to Christiane Baumeister, Department of Economics, University of Notre Dame, Notre Dame, IN 46556, USA; e-mail: cjsbaumeister@gmail.com.
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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.

Type
ET LECTURE
Information
Econometric Theory , Volume 40 , Issue 3 , June 2024 , pp. 472 - 510
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

This paper was presented as the Econometric Theory Lecture at the EC$^{2}$ 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.” We are thankful to Xin Gu, Frank Kleibergen, Lam Nguyen, Peter C.B. Phillips, Matthew Read, an anonymous co-editor, and three anonymous referees for very useful insights.

References

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