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A new test to detect changes in the covariance
structure of a time series is developed. The test
does not involve direct fitting of an assumed model
for the time series. It is based on detecting
changes in autocovariances calculated in a moving
window through the series. The use of standard tests
of time series change points is inappropriate
because of the correlations imposed by the moving
windows. This requires the development of new
adjustments to existing time series change point
tests. The ability of this moving window technique
to detect changes in the lag one autocovariance of
autoregressive and moving average time series is
studied. We illustrate the application of this new
test on UK Treasury bill rates and airline travel
data.
structure of a time series is developed. The test
does not involve direct fitting of an assumed model
for the time series. It is based on detecting
changes in autocovariances calculated in a moving
window through the series. The use of standard tests
of time series change points is inappropriate
because of the correlations imposed by the moving
windows. This requires the development of new
adjustments to existing time series change point
tests. The ability of this moving window technique
to detect changes in the lag one autocovariance of
autoregressive and moving average time series is
studied. We illustrate the application of this new
test on UK Treasury bill rates and airline travel
data.