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:: Volume 12, Issue 1 (9-2018) ::
J. of Stat. Sci. 2018, 12(1): 143-163 Back to browse issues page
Detection of Shocks in Structural Time Series Model Using State Space Forms
Reza Zabihi Moghadam , Rahim Chinipardaz, Gholamali Parham
Abstract:   (3346 Views)

In this paper a method has been given to detect the shocks in structural time series using Kalman filter algorithm. As the Kalman filter algorithm is used for state space forms which include ARMA models as an especial case, the suggested method can be used for more general time series than linear models. Five shocks; additive outlier, level change, seasonal change, periodic change and slope change have been reviewed with this method. The performance of suggested method has been shown via a simulation study. The marriage data set from England has been considered as a real data set to study.

Keywords: Kalman filter smoother, Outliers, Structural models, State space model, Structural breaks
Full-Text [PDF 239 kb]   (1365 Downloads)    
Type of Study: Research | Subject: Time Series
Received: 2014/06/12 | Accepted: 2018/04/2 | Published: 2018/04/19
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Zabihi Moghadam R, Chinipardaz R, Parham G. Detection of Shocks in Structural Time Series Model Using State Space Forms. J. of Stat. Sci.. 2018; 12 (1) :143-163
URL: http://jss.irstat.ir/article-1-303-en.html

Volume 12, Issue 1 (9-2018) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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