Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries
This paper provides an application of generalized space-time autoregressive (GSTAR) model on GDP data in West European countries. Preliminary model is identified by space-time ACF and space-time PACF of the sample, and model parameters are estimated using the least square method. The forecast perfor...
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Series: | Journal of Probability and Statistics |
Online Access: | http://dx.doi.org/10.1155/2012/867056 |
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doaj-565d849c7dbf4da4bc20f9153364b3352020-11-24T22:16:18ZengHindawi LimitedJournal of Probability and Statistics1687-952X1687-95382012-01-01201210.1155/2012/867056867056Application of Generalized Space-Time Autoregressive Model on GDP Data in West European CountriesNunung Nurhayati0Udjianna S. Pasaribu1Oki Neswan2Faculty of Science and Engineering, Jenderal Soedirman University, Purwokerto 53122, IndonesiaFaculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung 40132, IndonesiaFaculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung 40132, IndonesiaThis paper provides an application of generalized space-time autoregressive (GSTAR) model on GDP data in West European countries. Preliminary model is identified by space-time ACF and space-time PACF of the sample, and model parameters are estimated using the least square method. The forecast performance is evaluated using the mean of squared forecast errors (MSFEs) based on the last ten actual data. It is found that the preliminary model is GSTAR(2;1,1). As a comparison, the estimation and the forecast performance are also applied to the GSTAR(1;1) model which has fewer parameter. The results showed that the ASFE of GSTAR(2;1,1) is smaller than that of the order (1;1). However, the t-test value shows that the performance is significantly indifferent. Thus, due to the parsimony principle, the GSTAR(1;1) model might be considered as a forecasting model.http://dx.doi.org/10.1155/2012/867056 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nunung Nurhayati Udjianna S. Pasaribu Oki Neswan |
spellingShingle |
Nunung Nurhayati Udjianna S. Pasaribu Oki Neswan Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries Journal of Probability and Statistics |
author_facet |
Nunung Nurhayati Udjianna S. Pasaribu Oki Neswan |
author_sort |
Nunung Nurhayati |
title |
Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries |
title_short |
Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries |
title_full |
Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries |
title_fullStr |
Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries |
title_full_unstemmed |
Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries |
title_sort |
application of generalized space-time autoregressive model on gdp data in west european countries |
publisher |
Hindawi Limited |
series |
Journal of Probability and Statistics |
issn |
1687-952X 1687-9538 |
publishDate |
2012-01-01 |
description |
This paper provides an application of generalized space-time autoregressive (GSTAR) model on GDP data in West European countries. Preliminary model is identified by space-time ACF and space-time PACF of the sample, and model parameters are estimated using the least square method. The forecast performance is evaluated using the mean of squared forecast errors (MSFEs) based on the last ten actual data. It is found that the preliminary model is GSTAR(2;1,1). As a comparison, the estimation and the forecast performance are also applied to the GSTAR(1;1) model which has fewer parameter. The results showed that the ASFE of GSTAR(2;1,1) is smaller than that of the order (1;1). However, the t-test value shows that the performance is significantly indifferent. Thus, due to the parsimony principle, the GSTAR(1;1) model might be considered as a forecasting model. |
url |
http://dx.doi.org/10.1155/2012/867056 |
work_keys_str_mv |
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1725790738439995392 |