Robust Bayesian Analysis for Econometrics

Raffaella Giacomini, Toru Kitagawa () and Matthew Read
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Toru Kitagawa: http://www.homepages.ucl.ac.uk/~uctptk0/

No WP-2021-11, Working Paper Series from Federal Reserve Bank of Chicago

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 autoregressions (search for similar items in EconPapers)
JEL-codes: C11 C18 C52 (search for similar items in EconPapers)
Pages: 42
Date: 2021-08-23
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-isf and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Robust Bayesian Analysis for Econometrics (2021)
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DOI: 10.21033/wp-2021-11

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