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Robust Bayesian Inference in Proxy SVARs

Giacomini, R; Kitagawa, T; Read, M; (2021) Robust Bayesian Inference in Proxy SVARs. Journal of Econometrics 10.1016/j.jeconom.2021.02.003. (In press). Green open access

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Abstract

We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the parameters of interest are set-identified using external instruments, or ‘proxy SVARs’. Set-identification in these models typically occurs when there are multiple instruments for multiple structural shocks. Existing Bayesian approaches to inference in proxy SVARs require researchers to specify a single prior over the model’s parameters, but, under set-identification, a component of the prior is never revised. We extend the robust Bayesian approach to inference in set-identified models proposed by Giacomini and Kitagawa in press[a] – which allows researchers to relax potentially controversial point-identifying restrictions without having to specify an unrevisable prior – to proxy SVARs. We provide new results on the frequentist validity of the approach in proxy SVARs. We also explore the effect of instrument strength on inference about the identified set. We illustrate our approach by revisiting Mertens and Ravn (2013) and relaxing the assumption that they impose to obtain point identification.

Type: Article
Title: Robust Bayesian Inference in Proxy SVARs
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jeconom.2021.02.003
Publisher version: https://doi.org/10.1016/j.jeconom.2021.02.003
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10107783
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