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

Anne B. Koehler and Emily S. Murphree
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 37, No. 2 (1988), pp. 187-195
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2347338
Stable URL: http://www.jstor.org/stable/2347338
Page Count: 9
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
A Comparison of the Akaike and Schwarz Criteria for Selecting Model Order
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Abstract

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.

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