全球智库信息集成服务系统(G2TT): DP14626 Robust Bayesian Inference in Proxy SVARs

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP14626
	DP14626 Robust Bayesian Inference in Proxy SVARs
	Raffaella Giacomini; Toru Kitagawa; Matthew Read
发表日期	2020-04-16
出版年	2020
语种	英语
摘要	We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the parameters of interest are set-identified using external instruments, or ‘proxy SVARs’. Set-identification in these models typically occurs when there are multiple instruments for multiple structural shocks. Existing Bayesian approaches to inference in proxy SVARs require researchers to specify a single prior over the model’s parameters, but, under set-identification, a component of the prior is never revised. We extend the robust Bayesian approach to inference in set-identified models proposed by Giacomini and Kitagawa (2018) – which allows researchers to relax potentially con- troversial point-identifying restrictions without having to specify an unrevisable prior – to proxy SVARs. We provide new results on the frequentist validity of the approach in proxy SVARs. We also explore the effect of instrument strength on inference about the identified set. We illustrate our approach by revisiting Mertens and Ravn (2013) and relaxing the assumption that they impose to obtain point identification.
主题	Monetary Economics and Fluctuations
URL	https://cepr.org/publications/dp14626
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/543536
推荐引用方式 GB/T 7714	Raffaella Giacomini,Toru Kitagawa,Matthew Read. DP14626 Robust Bayesian Inference in Proxy SVARs. 2020.