Information and Prediction Criteria in Selecting the Forecasting Model
DOI:
https://doi.org/10.12775/DEM.2011.002Keywords
information and prediction criteria, accumulated prediction error, model selectionAbstract
The purpose of the paper it to compare the performance of both information and prediction criteria in selecting the forecasting model on empirical data for Poland when the data generating model is unknown. The attention will especially focus on the evolution of information
criteria (AIC, BIC) and accumulated prediction error (APE) for increasing sample sizes and rolling windows of different size, and also the impact of initial sample and rolling window sizes on the selection of forecasting model. The best forecasting model will be chosen from the set including three models: autoregressive model, AR (with or without a deterministic trend), ARIMA model and random walk (RW) model.
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