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Identification methods in time series models; the case of the Palestinian banking sector

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dc.contributor.author Ehab M. Abuzuiter
dc.date.accessioned 6/12/2020
dc.date.accessioned 6/12/2020
dc.date.accessioned 2021-01-07T16:07:15Z
dc.date.available 6/12/2020
dc.date.available 2021-01-07T16:07:15Z
dc.date.issued 11/14/2012
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/2187
dc.description.abstract Model identification method is the important step in time series modeling, Box-Jenkins identification method depends on examining the patterns of the sample autocorrelation function (SACF) and partial sample autocorrelation function (PSACF), In some cases, the behavior of SACF and PACF are similar which make the identification much more difficult. In our thesis, we studied three methods [ESACF, SCAN and MINIC] of identification, and concentrated on their performance for order selection of mixed ARIMA model. The order selection of ARIMA models by using the three methods were ARIMA(3.1.0), ARIMA(1.1.0) and ARIMA(2.1.1), ARIMA(1.1.1) from MINIC, SCAN and ESACF, respectively. An application on the three methods using real data is conducted. Also their performance for order selection of mixed ARIMA model are compared by AIC, BIC, ME, RMSE, MAE and MPA criteria. We found that SCAN method gives the best order selection for the ARIMA model. en_US
dc.language.iso en_US en_US
dc.publisher Batch2 en_US
dc.title Identification methods in time series models; the case of the Palestinian banking sector en_US
dc.type Thesis en_US


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