G2TT
来源类型Discussion paper
规范类型论文
来源IDDP16488
DP16488 Robust Bayesian Analysis for Econometrics
Raffaella Giacomini; Toru Kitagawa; Matthew Read
发表日期2021-08-27
出版年2021
语种英语
摘要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.
主题Monetary Economics and Fluctuations
URLhttps://cepr.org/publications/dp16488
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/545444
推荐引用方式
GB/T 7714
Raffaella Giacomini,Toru Kitagawa,Matthew Read. DP16488 Robust Bayesian Analysis for Econometrics. 2021.
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