Advances in Using Vector Autoregressions to Estimate Structural Magnitudes

Christiane Baumeister and James D. Hamilton

No 27014, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C11 C32 Q43 (search for similar items in EconPapers)
Date: 2020-04
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Published as Christiane Baumeister & James D. Hamilton, 2024. "ADVANCES IN USING VECTOR AUTOREGRESSIONS TO ESTIMATE STRUCTURAL MAGNITUDES," Econometric Theory, vol 40(3), pages 472-510.

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