Automatic detection and identification of shocks in Gaussian state-space models: A Bayesian approach

被引：1

作者：

Salvador, M ^{[1
]}

Gargallo, P ^{[1
]}

机构：

[1] Univ Zaragoza, Dept Stat Methods, Fac Econ & Business Studies, Zaragoza 50005, Spain

来源：

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | 2006年 / 22卷 / 01期

关键词：

Bayes factor; monitoring and intervention; model comparison; model selection; linear state-space models; Kalman filter;

D O I：

10.1002/asmb.608

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

An automatic monitoring and intervention algorithm that permits the supervision of very general aspects in an univariate linear Gaussian state-space model is proposed. The algorithm makes use of a model comparison and selection approach within a Bayesian framework. In addition, this algorithm incorporates the possibility of eliminating earlier interventions when subsequent evidence against them comes to light. Finally, the procedure is illustrated with two empirical examples taken from the literature. Copyright (C) 2005 John Wiley & Sons, Ltd.

引用

页码：17 / 39

页数：23

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