Autoregressive Order Selection Criteria and their Performances at Different Sizes of Time Series

P. Kiran Kumar¹ , B. Sarojamma² , P. Ramakrishna Reddy³

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.4 , pp.198-208, Aug-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v5i4.198208

Online published on Aug 31, 2018

Copyright © P. Kiran Kumar, B. Sarojamma, P. Ramakrishna Reddy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In the present study, the most popular model selection criteria are used as the autoregressive order selection criteria and the performances of time series model selection criteria at different sizes of the same time series are observed. In time series analysis and forecasting, selecting the most suitable model for a given time series and size of available time series plays a vital role. We verified that Final Prediction Error Criterion and Akaike’s Information Criterion are asymptotically equivalent and Akaike’s Information Criterion and Bias-Corrected Akaike’s Information Criterion are asymptotically equivalent when the size of time series is large with respect to the dimension of the parameters of the autoregressive process using empirical study. All the time series model selection criteria presented in the paper are evaluated by log-likelihood function.

Key-Words / Index Term :
FPE, AIC, AICc, BIC, HQC, and MDL

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