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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.
Authors

Research Associate University College London
Raffaella is a Professor in the Department of Economics at UCL and a Senior Economist at the Federal Reserve Bank of Chicago.

Research Associate University College London and Brown University
Toru is a Research Associate of the IFS, a Professor of Economics at UCL and an Associate Professor in the Department of Economics at Brown University

Matthew Read
Working Paper details
- DOI
- 10.1920/wp.cem.2019.3819
- Publisher
- The IFS
Suggested citation
R, Giacomini and T, Kitagawa and M, Read. (2019). Robust Bayesian Inference in Proxy SVARs. London: The IFS. Available at: https://ifs.org.uk/publications/robust-bayesian-inference-proxy-svars (accessed: 8 February 2025).
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