Monitoring unit root and multiple structural changes: An information criterion approach

An information criterion-based model selection method is proposed for monitoring unit root and multiple structural changes. In this method, a battery of possible models is considered by changing the integration order (I(0) or I(1)) and the combinations of change points. Next, the best model is selected from among alternative models via a modified Bayesian information criterion (BIC). Accordingly, on the basis of the selected model, the process that generates the observed time series is determined. The BIC is modified in order to adjust the frequency count of incorrectly selecting stationary models via the conventional BIC. The simulation results of monitoring unit root and structural change suggest that the proposed method outperforms the conventional hypothesis testing method in terms of detection accuracy and detection speed. Furthermore, the empirical results suggest that the proposed method exhibits better performances with regard to detection stability and forecastability.