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 (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

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 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

Time series modelling and forecasting of Sarawak black pepper price

Pepper is an important agriculture commodity especially for the state of Sarawak. It is important to forecast its price, as this could help the policy makers in coming up with production and marketing plan to improve the Sarawak’s economy as well as the farmers’ welfare. In this paper, we take up time series modelling and forecasting of the Sarawak black pepper price. Our empirical results show that Autoregressive Moving Average (ARMA) time series models fit the price series well and they have correctly predicted the future trend of the price series within the sample period of study. Amongst a group of 25 fitted models, ARMA (1, 0) model is selected based on post-sample forecast criteria

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 (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