Arbeitspapier
Robust Bayesian inference in proxy SVARs
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-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 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.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP38/19
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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Giacomini, Raffaella
Kitagawa, Toru
Read, Matthew
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2019
- DOI
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doi:10.1920/wp.cem.2019.3819
- Handle
- Last update
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10.03.2025, 11:45 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Giacomini, Raffaella
- Kitagawa, Toru
- Read, Matthew
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2019
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Are Fiscal Multipliers Estimated with Proxy-SVARs Robust?
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Ensemble MCMC sampling for robust Bayesian inference
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The R Package Mitisem: Efficient and Robust Simulation Procedures for Bayesian Inference
The R-package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference
Analysis of variance for bayesian inference
Bayesian inference for the tangent portfolio
Robust inference with clustered data
Robust inference with clustered data
Are Fiscal Multipliers Estimated with Proxy-SVARs Robust?
Robust Bayesian inference in proxy SVARs
Inference in Bayesian proxy-SVARs
Ensemble MCMC sampling for robust Bayesian inference
Robust Bayesian inference for set-identified models
Robust Bayesian inference for set-identified models
The R Package Mitisem: Efficient and Robust Simulation Procedures for Bayesian Inference
The R-package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference
Analysis of variance for bayesian inference
Bayesian inference for the tangent portfolio
Robust inference with clustered data
Robust inference with clustered data