A Comparison of the Akaike and Schwarz Criteria for Selecting Model Order

Abstract

SUMMARY The object of this paper is to compare the Akaike information criterion (AIC) and the Schwarz information criterion (SIC) when they are applied to the crucial and difficult task of choosing an order for a model in time series analysis. These order selection criteria are used to fit state space models. Models are fitted to a set of monthly time series randomly selected from the series used in the Makridakis competition (1982). All series are composed of real data. The AIC and SIC indicate different model orders in 27% of the cases. The forecasting accuracy is compared for these cases. The results of this comparison show that it is preferable to apply the SIC, which leads to lower order models for forecasting.