Changes of Variance in First-Order Autoregressive Time Series Models-With an Application

Abstract

SUMMARY A two-stage method is presented for detecting step changes of variance in first-order autoregressive time series models. Potential change points are initially located using a moving-block procedure. Given initial change points, an iterative likelihood argument is used to develop estimators of the change points, variances and autoregressive parameters. The efficacy of the method is examined with computer simulation experiments, and a numerical example using stock market data is discussed.