The Performance of AICC As Order Determination Criterion in ARMA time series models

This study is undertaken with the objective of investigating the performance of Akaike’s Information Corrected Criterion (AICC) as an order determination criterion for the selection of Autoregressive Moving-average or ARMA (p, q) time series models. A simulation investigation was carried out to dete...

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Main Authors: Khim Sen, Liew, Shitana, Mahnendran
Format: Article
Language:English
Published: Pertanika J. Sci. & Technol 2002
Subjects:
Online Access:http://ir.unimas.my/id/eprint/1808/
http://ir.unimas.my/id/eprint/1808/1/The%2BPerformance%2Bof%2BAICC%2BAs%2BOrder%2BDetermination%2BCriterion%2Bin%2BARMA%2Btime%2Bseries%2Bmodels%2B%2528abstract%2529%20%281%29.pdf
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author Khim Sen, Liew
Shitana, Mahnendran
author_facet Khim Sen, Liew
Shitana, Mahnendran
author_sort Khim Sen, Liew
building UNIMAS Institutional Repository
collection Online Access
description This study is undertaken with the objective of investigating the performance of Akaike’s Information Corrected Criterion (AICC) as an order determination criterion for the selection of Autoregressive Moving-average or ARMA (p, q) time series models. A simulation investigation was carried out to determine the probability of the AICC statistic picking up the true model. Results obtained showed that the probability of the AICC criterion picking up the correct model was moderately good. The problem of over parameterization existed but under parameterization was found to be minimal. Hence, for any two comparable models, it is always safe to choose the one with lower order of p and q.
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spelling unimas-18082016-12-28T04:11:22Z http://ir.unimas.my/id/eprint/1808/ The Performance of AICC As Order Determination Criterion in ARMA time series models Khim Sen, Liew Shitana, Mahnendran AC Collections. Series. Collected works H Social Sciences (General) HB Economic Theory This study is undertaken with the objective of investigating the performance of Akaike’s Information Corrected Criterion (AICC) as an order determination criterion for the selection of Autoregressive Moving-average or ARMA (p, q) time series models. A simulation investigation was carried out to determine the probability of the AICC statistic picking up the true model. Results obtained showed that the probability of the AICC criterion picking up the correct model was moderately good. The problem of over parameterization existed but under parameterization was found to be minimal. Hence, for any two comparable models, it is always safe to choose the one with lower order of p and q. Pertanika J. Sci. & Technol 2002-01 Article NonPeerReviewed text en http://ir.unimas.my/id/eprint/1808/1/The%2BPerformance%2Bof%2BAICC%2BAs%2BOrder%2BDetermination%2BCriterion%2Bin%2BARMA%2Btime%2Bseries%2Bmodels%2B%2528abstract%2529%20%281%29.pdf Khim Sen, Liew and Shitana, Mahnendran (2002) The Performance of AICC As Order Determination Criterion in ARMA time series models. Pertanika J. Sci. & Technol, 10 (1). pp. 25-33.
spellingShingle AC Collections. Series. Collected works
H Social Sciences (General)
HB Economic Theory
Khim Sen, Liew
Shitana, Mahnendran
The Performance of AICC As Order Determination Criterion in ARMA time series models
title The Performance of AICC As Order Determination Criterion in ARMA time series models
title_full The Performance of AICC As Order Determination Criterion in ARMA time series models
title_fullStr The Performance of AICC As Order Determination Criterion in ARMA time series models
title_full_unstemmed The Performance of AICC As Order Determination Criterion in ARMA time series models
title_short The Performance of AICC As Order Determination Criterion in ARMA time series models
title_sort performance of aicc as order determination criterion in arma time series models
topic AC Collections. Series. Collected works
H Social Sciences (General)
HB Economic Theory
url http://ir.unimas.my/id/eprint/1808/
http://ir.unimas.my/id/eprint/1808/1/The%2BPerformance%2Bof%2BAICC%2BAs%2BOrder%2BDetermination%2BCriterion%2Bin%2BARMA%2Btime%2Bseries%2Bmodels%2B%2528abstract%2529%20%281%29.pdf