ABSTRACT
Records of real-world phenomena can mostly be categorized as nonstationary time series. The simplest approach to modeling nonstationary time series is to partition the time interval into several subintervals of appropriate size, on the assumption that the time series are stationary on each subinterval. Then, by fitting an AR model to each subinterval, we can obtain a series of models that approximate nonstationary time series. This chapter shows two modeling methods for the analysis of nonstationary time series, namely, a model for roughly deciding on the number of subintervals and the locations of their endpoints and a model for precisely estimating the change point. A locally stationary AR model is a nonstationary time series model which has the property that, on each appropriately constructed sub-interval, it is stationary and can be modeled by an AR model on each of these subintervals.
