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Automatic detection and identification of shocks in Gaussian state‐space models: a Bayesian approach
Author(s) -
Salvador Manuel,
Gargallo Pilar
Publication year - 2005
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.608
Subject(s) - bayesian probability , univariate , state space , state space representation , gaussian , computer science , identification (biology) , model selection , space (punctuation) , algorithm , bayesian inference , selection (genetic algorithm) , econometrics , artificial intelligence , machine learning , multivariate statistics , mathematics , statistics , quantum mechanics , biology , operating system , physics , botany
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 © 2005 John Wiley & Sons, Ltd.