ﺷﻨﺎﺳﺎﯾﯽ اﻧﻮاع ﺗﻐﯿﯿﺮات ﺗﺄﺛﯿﺮ ﮔﺬار ﺑﺮ رﻓﺘﺎر ﻣﺪل ﻫﺎي ﺳﺮي زﻣﺎﻧﯽ

Detection of Shocks in Structural Time Series Model Using State Space Forms

ﭼﮑﯿﺪه: در اﯾﻦ ﻣﻘﺎﻟﻪ روﺷﯽ ﺑﺮاي اﺳﺘﻔﺎده از ﺧﺮوﺟﯽ ﻫﺎي ﮐﺎﻟﻤﻦ ﻓﯿﻠﺘﺮ ﺑﺮاي ﺷﻨﺎﺳﺎﯾﯽ ﺗﻐﯿﯿﺮات ﺗﺄﺛﯿﺮ ﮔﺬار ﺳﺮي زﻣﺎﻧﯽ اراﺋﻪ ﺷﺪه اﺳﺖ. از آن ﺟﺎ ﮐﻪ اﻟﮕﻮرﯾﺘﻢ ﮐﺎﻟﻤﻦ ﻓﯿﻠﺘﺮ ﺑﺮاي ﺗﺤﻠﯿﻞ ﻣﺪل ﻫﺎي ﻓﻀﺎي ﺣﺎﻟﺖ ﺑﻪ ﮐﺎر ﻣﯽ رود ﮐﻪ ﻣﺪل ﻫﺎي ﺧﻄﯽARMA را ﭘﻮﺷﺶ ﻣﯽ دﻫﺪ، اﺳﺘﻔﺎده از اﯾﻦ روش ﻣﯽ ﺗﻮاﻧﺪ ﺑﺮاي ﺷﻨﺎﺳﺎﯾﯽ ﺗﻐﯿﯿﺮات از ﺟﻤﻠﻪ ﻣﻘﺎدﯾﺮ ﭘﺮت ﺑﻪ ﮐﺎر رود. در اﯾﻦ ﻣﻘﺎﻟﻪ روش ﭘﯿﺸﻨﻬﺎد ﺷﺪه ﺑﺮاي ﺷﻨﺎﺳﺎﯾﯽ ﭘﻨﺞ ﺗﻐﯿﯿﺮ: ﻧﻘﻄﻪ ﭘﺮت ﺟﻤﻊ ﭘﺬﯾﺮ، ﺗﻐﯿﯿﺮ ﺳﻄﺢ، ﺗﻐﯿﯿﺮات ﻓﺼﻠﯽ، ﺗﻐﯿﯿﺮ دوره و ﺷﯿﺐ ﻧﺎﮔﻬﺎﻧﯽ ﺳﺮي زﻣﺎﻧﯽ اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﺗﻮاﻧﺎﯾﯽ روش ﭘﯿﺸﻨﻬﺎدي در ﯾﺎﻓﺘﻦ ﻧﻘﺎط ﺗﺄﺛﯿﺮ ﮔﺬار ﺑﺎ اﺳﺘﻔﺎده ﺷﺒﯿﻪ ﺳﺎزي ﻧﺸﺎن داده ﺷﺪ. ﺑﻪ ﻋﻨﻮان ﯾﮏ ﻣﺜﺎل واﻗﻌﯽ داده ﻫﺎي ازدواج در ﮐﺸﻮر اﻧﮕﻠﯿﺲ ﻣﻮرد ﺑﺮرﺳﯽ ﻗﺮار ﮔﺮﻓﺖ.

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.