Forecasting ARMA models: A comparative study of information criteria focusing on MDIC
Date
2010Source
Journal of Statistical Computation and SimulationVolume
80Issue
1Pages
61-73Google Scholar check
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This paper deals with the implementation of model selection criteria to data generated by ARMA processes. The recently introduced modified divergence information criterion is used and compared with traditional selection criteria like the Akaike information criterion (AIC) and the Schwarz information criterion (SIC). The appropriateness of the selected model is tested for one- and five-step ahead predictions with the use of the normalized mean squared forecast errors (NMSFE). © 2010 Taylor & Francis.