A comparison of Box-Jenkins and objective methods for determining the order of a non-seasonal ARMA model

This paper evaluates different procedures for selecting the order of a non-seasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized Box-Jenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. The results show that many objective selection criteria provide structures equal or superior to the time-consuming BJ method. For the sets of data used in this study, the influence of parsimony in time-series forecasting is also examined. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.