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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Main Authors: Nunung Nurhayati, Udjianna S. Pasaribu, Oki Neswan
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2012/867056
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spelling 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
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AT udjiannaspasaribu applicationofgeneralizedspacetimeautoregressivemodelongdpdatainwesteuropeancountries
AT okineswan applicationofgeneralizedspacetimeautoregressivemodelongdpdatainwesteuropeancountries
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