Cointegration rank switching model: an application to forecasting interest rates

This paper proposes a new forecasting method in which the cointegration rank switches at unknown times. In this method, time series observations are divided into several segments, and a cointegrated vector autoregressive model is fitted to each segment. The goodness of fit of the global model, consisting of local models with different cointegration ranks, is evaluated using the information criterion (IC). The division that minimizes the IC defines the best model. The results of an empirical application to the US term structure of interest rates and a Monte Carlo simulation suggest the efficacy as well as the limitations of the proposed method.