New Procedure to Improve the Order Selection of Autoregressive Time Series Model

Abstract

Problem statement: We propose new approach could be used to guide the selection of the "true" order of autoregressive model for different sample size. Approach: We used simulation study to compare four model selection criteria with and without the help of the new approach. The comparison of the four model selection criteria was in terms of their percentage of number of times that they identify the "true" order of autoregressive model with and without the help of the new approach. Results: The simulation results indicate that overall, the new proposed approach showed very good performance with all the four model selection criteria comparing to their performance without the help of the new approach, where the SBC, AICC and HQIC criteria provided the best performance for all the cases. Conclusion: The main result of our article is that we recommend using the new proposed approach with SBC, AICC and HQIC criteria as a standard procedure to identify the "true" order of autoregressive model.