Detecting Autocovariance Change in Time Series

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.

Autorentext

Wisam R. Yaghi, Ph.D.: Studied Statistics at American University. Statistician at Freddie Mac. Robert W. Jernigan holds a Ph.D. from the University of South Florida and is currently Professor of Statistics at American University. Research interests: Statistical computing, mathematical ecology and evolution, environmental statistics.

Klappentext

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.