Time-Series Forecast Jointly Allowing the Unit-Root Detection and the Box-Cox Transformation

摘要

An information-criterion-based model-selection method is presented for forecasting that allows for the unit-root detection and the Box-Cox transformation simultaneously. In this method, a battery of alternative models with and without unit root is considered changing the order of autoregressive process and the Box-Cox parameter, and the best model is selected using information criteria. Simulation results suggest that the Bayesian information criterion (BIC) outperforms the bias-corrected Akaike information criterion (AICc) and that the augmented Dickey-Fuller test performs worse in the case of incorrect data transformation. The results of forecasting quarterly time series of industrial production indicate that the BIC-based method outperforms the other conventional methods.