Journal of Statistical Computation and Simulation (2005), 75(10), 831-840.
Baki Billah1, Rob J. Hyndman1 and Anne B. Koehler2
- Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia.
- Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH 45056, USA.
Abstract: In this paper, we propose a new Empirical Information Criterion (EIC) for model selection which penalizes the likelihood of the data by a function of the number of parameters in the model. It is designed to be used where there are a large number of time series to be forecast. However, a bootstrap version of the EIC can be used where there is a single time series to be forecast. The EIC provides a data-driven model selection tool that can be tuned to the particular forecasting task. We compare the EIC with other model selection criteria including Akaike’s Information Criterion (AIC) and Schwarz’s Bayesian Information Criterion (BIC). The comparisons show that for the M3 forecasting competition data, the EIC outperforms both the AIC and BIC, particularly for longer forecast horizons. We also compare the criteria on simulated data and find that the EIC does better than existing criteria in that case also.
Keywords: exponential smoothing; forecasting; information criteria; M3 competition; model selection.