Advances in using vector autoregressions to estimate structural magnitudes

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dc.contributor.author Baumeister, Christiane
dc.contributor.author Hamilton, James D.
dc.date.accessioned 2024-04-24T12:16:33Z
dc.date.available 2024-04-24T12:16:33Z
dc.date.issued 2024-06
dc.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". en_US
dc.description.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. en_US
dc.description.department Economics en_US
dc.description.librarian hj2024 en_US
dc.description.sdg SDG-08:Decent work and economic growth en_US
dc.description.uri https://www.cambridge.org/core/journals/econometric-theory en_US
dc.identifier.citation Baumeister, C. & Hamilton, J.D. Advances in using vector autoregressions to estimate structural magnitudes. Econometric Theory, vol. 40 , no. 3 , June 2024, pp. 472 - 510, DOI: https://doi.org/10.1017/S026646662200055X. en_US
dc.identifier.issn 0266-4666 (print)
dc.identifier.issn 1469-4360 (online)
dc.identifier.other 10.1017/S026646662200055X
dc.identifier.uri http://hdl.handle.net/2263/95751
dc.language.iso en en_US
dc.publisher Cambridge University Press en_US
dc.rights © The Author(s), 2022. Published by Cambridge University Press. en_US
dc.subject Vector autoregressive (VAR) en_US
dc.subject Structural vector autoregressions en_US
dc.subject Bayesian analysis en_US
dc.subject Identification en_US
dc.subject Elasticities en_US
dc.subject Sign restrictions en_US
dc.subject SDG-08: Decent work and economic growth en_US
dc.title Advances in using vector autoregressions to estimate structural magnitudes en_US
dc.type Postprint Article en_US


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