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Robust Bayesian Inference in Proxy SVARs

Giacomini, R; Kitagawa, T; Read, M; (2019) Robust Bayesian Inference in Proxy SVARs. (Cemmap Working Paper 38/19). Institute for Fiscal Studies: London, UK.

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Abstract

We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the impulse responses or forecast error variance decompositions of interest are set-identified using external instruments (or ‘proxy SVARs’). Existing Bayesian approaches to inference in proxy SVARs require researchers to specify a single prior over the model’s parameters. When parameters are set-identified, a component of the prior is never updated by the data. Giacomini and Kitagawa (2018) propose a method for robust Bayesian inference in set-identifed models that delivers inference about the identified set for the parameter of interest. We extend this approach to proxy SVARs, which allows researchers to relax potentially controversial point-identifying restrictions without having to specify an unrevisable prior. We also explore the effect of instrument strength on posterior inference. We illustrate our approach by revisiting Mertens and Ravn (2013) and relaxing the assumption that they impose to obtain point identification.

Type: Working / discussion paper
Title: Robust Bayesian Inference in Proxy SVARs
DOI: 10.1920/wp.cem.2019.3819
Publisher version: https://doi.org/10.1920/wp.cem.2019.3819
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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/10087228
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