Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/251005 
Year of Publication: 
2021
Series/Report no.: 
Working Paper No. WP 2021-11
Publisher: 
Federal Reserve Bank of Chicago, Chicago, IL
Abstract: 
We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the robust Bayesian analysis. We consider both a general set-up for Bayesian statistical decisions and inference and the special case of set-identified structural models. We provide new results that can be used to derive and compute the set of posterior moments for sensitivity analysis and to compute the optimal statistical decision under multiple priors. The paper ends with a self-contained discussion of three different approaches to robust Bayesian inference for setidentified structural vector autoregressions, including details about numerical implementation and an empirical illustration.
Subjects: 
ambiguity
Bayesian robustness
statistical decision theory
identifying restrictions
multiple priors
structural vector autoregression
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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