Working Paper: CEPR ID: DP16488
Authors: Raffaella Giacomini; Toru Kitagawa; Matthew Read
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 set- identified structural vector autoregressions, including details about numerical implementation and an empirical illustration.
Keywords: ambiguity; bayesian robustness; statistical decision theory; identifying restrictions; multiple priors; structural vector autoregression
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| robust Bayesian outputs from multiple priors (C11) | more informative inference (D83) |
| set of posterior means (C11) | consistent estimators of identified sets (C51) |
| choice of priors (C11) | influences posterior distributions (C46) |
| robust Bayesian methods (C11) | nuanced understanding of uncertainty surrounding parameter estimates (C51) |
| robustness of Bayesian credible regions (C11) | valid frequentist coverage of the true identified set (C46) |