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Forecast Performance Between SARIMA and SETAR Models: An Application to Ghana Inflation RateAIDOO, ERIC January 2011 (has links)
In recent years, many research works such as Tiao and Tsay (1994), Stock and Watson (1999), Chen et al. (2001), Clements and Jeremy (2001), Marcellino (2002), Laurini and Vieira (2005) and others have described the dynamic features of many macroeconomic variables as nonlinear. Using the approach of Keenan (1985) and Tsay (1989) this study shown that Ghana inflation rates from January 1980 to December 2009 follow a threshold nonlinear process. In order to take into account the nonlinearity in the inflation rates we then apply a two regime nonlinear SETAR model to the inflation rates and then study both in-sample and out-of-sample forecast performance of this model by comparing it with the linear SARIMA model. Based on the in-sample forecast assessment from the linear SARIMA and the nonlinear SETAR models, the forecast measure MAE and RMSE suggest that the nonlinear SETAR model outperform the linear SARIMA model. Also using multi-step-ahead forecast method we predicted and compared the out-of-sample forecast of the linear SARIMA and the nonlinear SETAR models over the forecast horizon of 12 months during the period of 2010:1 to 2010:12. From the results as suggested by MAE and RMSE, the forecast performance of the nonlinear SETAR models is superior to that of the linear SARIMA model in forecasting Ghana inflation rates. Thought the nonlinear SETAR model is superior to the SARIMA model according to MAE and RMSE measure but using Diebold-Mariano test, we found no significant difference in their forecast accuracy for both in-sample and out-of-sample.
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時間序列模型建立之各種分析方法之比較與實證研究徐瑞玲, XU, RUI-LING Unknown Date (has links)
時間數列分析自一九七0年Box-Jenkins 發展出自我迴歸移動平均整合模式(簡稱A
RIMA(p,d,q))建立法後,便更普遍地應用於經濟、企管、工程及物理等
相關領域上。但利用Box-Jenkins 的鑑定方法一般只對MA或AR模型有效,而對混
合的ARMA模型則不適用。其後陸續有統計學者提出不同的鑑定方法,但都無法有
效地決定P、d、q階數。
直至一九八四年以後,Tsay和Tiao兩位學者才又提出了一套有效的鑑定法則,利用擴
展的樣本自我相關函數(Extended Sample Autocorrelation Function)或正規分析
(Canonical Analysis)求出的最小正規相關係數(The Smallest Canonical Corr-
elation )做為鑑定p、d、q的準則。這兩種方法的優點皆為可直接處理平穩或非
平穩型時間數列,而不用事先決定差分的階數,而且對混合ARIMA模型亦有效。
對於有異常點(Outlier )存在的時間數列,其可能由於某些外在的介入因素所引起
,而ARIMA模型對資料的配適是不足夠的。因此該如何發現異常點的存在及加入
合理的介入模式亦構成了模型鑑定的問題。本文除對Tsay和Tiao的方法做一說明外,
亦利用其鑑定方法對存在有異常點的時間數列做一分析,並由實證研究探討其對季節
模型的鑑定效果。
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Tse Keh Nay-European Relations and Ethnicity: 1790s-2009Sims, Daniel Unknown Date
No description available.
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Tse Keh Nay-European Relations and Ethnicity: 1790s-2009Sims, Daniel 06 1900 (has links)
This thesis examines Tse Keh Nay (Sekani) ethnic identity over three periods of Aboriginal-European relations: the fur trade period, the missionary period, and the treaty and reserve period. It examines the affects these three periods have had on the Tse Keh Nay as an ethnic group in four chapters, the first two dealing with the fur trade and missionary periods, and the last two with the treaty and reserve aspects of the treaty and reserve period. In it I argue that during the first two periods wider Tse Keh Nay ethnic identity was reinforced, while during the latter period local Tse Keh Nay identities were reinforced through government policies that dealt with Tse Keh Nay subgroups on a regional and localized basis. Despite this shift in emphasis, wider Tse Keh Nay ethnic identity has remained, proving that Tse Keh Nay ethnic identity is both situational and dynamic. / History
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