DP16488 Robust Bayesian Analysis for Econometrics
|Author(s):||Raffaella Giacomini, Toru Kitagawa, Matthew Read|
|Publication Date:||August 2021|
|Date Revised:||August 2021|
|Programme Areas:||Monetary Economics and Fluctuations|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=16488|
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.