Robust Bayesian analysis for econometrics

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. WP 2021-11

Classification and Topics

• Classification: Wirtschaft
• Subject: ambiguity ; Bayesian robustness ; statistical decision theory ; identifying restrictions ; multiple priors ; structural vector autoregression

Contributors, Location and Time

• Event: Geistige Schöpfung
• (who): Giacomini, Raffaella ; Kitagawa, Toru ; Read, Matthew
• Event: Veröffentlichung
• (who): Federal Reserve Bank of Chicago
• (where): Chicago, IL
• (when): 2021

Further information

• DOI: doi:10.21033/wp-2021-11
• Handle: http://hdl.handle.net/10419/251005
• Last update: 10.03.2025, 11:42 AM CET