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Performance of Autoregressive Order Selection Criteria: A Simulation Study

Proper selection of autoregressive order plays a crucial role in econometrics modeling cycles and testing procedures. This paper compares the performance of various autoregressive order selection criteria in selecting the true order. This simulation study shows that Schwarz information criterion (S...

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Main Authors: Liew, Venus Khim-Sen, Shitan, Mahnendran, Choong, Chee-Keong, Hooy, Chee-Wooi
Format: E-Article
Language:English
Published: Universiti Putra Malaysia Press 2008
Subjects:
HB Economic Theory
Online Access:http://ir.unimas.my/id/eprint/18654/8/Performance%20of%20Autoregressive%20Order%20Selection%20Criteria%20%28abstract0.pdf
http://ir.unimas.my/id/eprint/18654/
https://www.researchgate.net/publication/255584762
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spelling my.unimas.ir.186542017-11-20T07:48:36Z http://ir.unimas.my/id/eprint/18654/ Performance of Autoregressive Order Selection Criteria: A Simulation Study Liew, Venus Khim-Sen Shitan, Mahnendran Choong, Chee-Keong Hooy, Chee-Wooi HB Economic Theory Proper selection of autoregressive order plays a crucial role in econometrics modeling cycles and testing procedures. This paper compares the performance of various autoregressive order selection criteria in selecting the true order. This simulation study shows that Schwarz information criterion (SIC) , final prediction error (FPE) , Hannan-Qiunn criterion (HQC) and Bayesian information criterion (BIC) have considerable high performance in selecting the true autoregressive order, even if the sample size is small, whereas Akaike’s information criterion (AIC) over-estimated the true order with a probability of more than two-thirds. Further, this simulation study also shows that the probability of these criteria (except AIC) in correctly estimating the true order approaches one as sample size grows. Generally, these findings show that the most commonly used AIC might yield misleading policy conclusions due to its unsatisfactory performance. We note here that out of a class of commonly used criteria, BIC performs the best for a small sample size of 25 observations. Universiti Putra Malaysia Press 2008 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/18654/8/Performance%20of%20Autoregressive%20Order%20Selection%20Criteria%20%28abstract0.pdf Liew, Venus Khim-Sen and Shitan, Mahnendran and Choong, Chee-Keong and Hooy, Chee-Wooi (2008) Performance of Autoregressive Order Selection Criteria: A Simulation Study. Pertanika Journal of Science & Technology, 16 (2). pp. 171-176. ISSN 0128-7680 https://www.researchgate.net/publication/255584762
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic HB Economic Theory
spellingShingle HB Economic Theory
Liew, Venus Khim-Sen
Shitan, Mahnendran
Choong, Chee-Keong
Hooy, Chee-Wooi
Performance of Autoregressive Order Selection Criteria: A Simulation Study
description Proper selection of autoregressive order plays a crucial role in econometrics modeling cycles and testing procedures. This paper compares the performance of various autoregressive order selection criteria in selecting the true order. This simulation study shows that Schwarz information criterion (SIC) , final prediction error (FPE) , Hannan-Qiunn criterion (HQC) and Bayesian information criterion (BIC) have considerable high performance in selecting the true autoregressive order, even if the sample size is small, whereas Akaike’s information criterion (AIC) over-estimated the true order with a probability of more than two-thirds. Further, this simulation study also shows that the probability of these criteria (except AIC) in correctly estimating the true order approaches one as sample size grows. Generally, these findings show that the most commonly used AIC might yield misleading policy conclusions due to its unsatisfactory performance. We note here that out of a class of commonly used criteria, BIC performs the best for a small sample size of 25 observations.
format E-Article
author Liew, Venus Khim-Sen
Shitan, Mahnendran
Choong, Chee-Keong
Hooy, Chee-Wooi
author_facet Liew, Venus Khim-Sen
Shitan, Mahnendran
Choong, Chee-Keong
Hooy, Chee-Wooi
author_sort Liew, Venus Khim-Sen
title Performance of Autoregressive Order Selection Criteria: A Simulation Study
title_short Performance of Autoregressive Order Selection Criteria: A Simulation Study
title_full Performance of Autoregressive Order Selection Criteria: A Simulation Study
title_fullStr Performance of Autoregressive Order Selection Criteria: A Simulation Study
title_full_unstemmed Performance of Autoregressive Order Selection Criteria: A Simulation Study
title_sort performance of autoregressive order selection criteria: a simulation study
publisher Universiti Putra Malaysia Press
publishDate 2008
url http://ir.unimas.my/id/eprint/18654/8/Performance%20of%20Autoregressive%20Order%20Selection%20Criteria%20%28abstract0.pdf
http://ir.unimas.my/id/eprint/18654/
https://www.researchgate.net/publication/255584762
_version_ 1644512903569604608
score 13.211869

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