全球智库信息集成服务系统(G2TT): Bayesian and Frequentist Inference in Partially Identified Models

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w14882
来源ID	Working Paper 14882
	Bayesian and Frequentist Inference in Partially Identified Models
	Hyungsik Roger Moon; Frank Schorfheide
发表日期	2009-04-16
出版年	2009
语种	英语
摘要	A large sample approximation of the posterior distribution of partially identified structural parameters is derived for models that can be indexed by a finite-dimensional reduced form parameter vector. It is used to analyze the differences between frequentist confidence sets and Bayesian credible sets in partially identified models. A key difference is that frequentist set estimates extend beyond the boundaries of the identified set (conditional on the estimated reduced form parameter), whereas Bayesian credible sets can asymptotically be located in the interior of the identified set. Our asymptotic approximations are illustrated in the context of simple moment inequality models and a numerical illustration for a two-player entry game is provided.
主题	Econometrics ; Estimation Methods
URL	https://www.nber.org/papers/w14882
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/572558
推荐引用方式 GB/T 7714	Hyungsik Roger Moon,Frank Schorfheide. Bayesian and Frequentist Inference in Partially Identified Models. 2009.