Robust Bayesian Inference in Proxy SVARs

Raffaella Giacomini (), Toru Kitagawa () and Matthew Read
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Toru Kitagawa: Institute for Fiscal Studies and University College London

No CWP38/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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-identi?ed 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-identi?ed, 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 identi?ed 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 e?ect 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 identi?cation.

Date: 2019-07-23
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (12)

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Related works:
Journal Article: Robust Bayesian inference in proxy SVARs (2022)
Working Paper: Robust Bayesian Inference in Proxy SVARs (2020)
Working Paper: Robust Bayesian inference in proxy SVARs (2020)
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