Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/wiley/automatic-detection-and-identification-of-shocks-in-gaussian-state-CaqUbtFjfL
References (10)
-
C. Ng, P. Young (1990)
Recursive estimation and forecasting of non‐stationary time seriesJournal of Forecasting, 9
-
Kass Kass, Raftery Raftery (1995)
Bayes' factorsJournal of the American Statistical Association, 90
-
A. Atkinson, S. Koopman, N. Shephard (1997)
Detecting shocks: Outliers and breaks in time seriesJournal of Econometrics, 80
-
A. Pole, M. West, J. Harrison (1994)
Applied Bayesian Forecasting and Time Series Analysis -
P. Gargallo, M. Salvador (2003)
Monitoring Residual Autocorrelations in Dynamic Linear ModelsCommunications in Statistics - Simulation and Computation, 32
-
A. Harvey, J. Durbin (1986)
The Effects of Seat Belt Legislation on British Road Casualties: A Case Study in Structural Time Series ModellingJournal of The Royal Statistical Society Series A-statistics in Society, 149
-
(2002)
A New Approach to Linear Filtering and Prediction Problems -
Nathan Balke (1993)
Detecting Level Shifts in Time SeriesJournal of Business & Economic Statistics, 11
-
P. Jong, Jeremy Penzer (1998)
Diagnosing Shocks in Time SeriesJournal of the American Statistical Association, 93
-
D. Madigan, A. Raftery (1994)
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's WindowJournal of the American Statistical Association, 89
- Publisher
- Wiley
- Copyright
- Copyright © 2005 John Wiley & Sons, Ltd.
- ISSN
- 1524-1904
- eISSN
- 1526-4025
- DOI
- 10.1002/asmb.608
- Publisher site
- See Article on Publisher Site
Abstract
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.
Journal
Applied Stochastic Models in Business and Industry – Wiley
Published: Jan 1, 2006