Detecting abrupt changes in a piecewise locally stationary time series

Michael Last and Robert Shumway

Journal of Multivariate Analysis, 2008, vol. 99, issue 2, 191-214

Abstract: Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback-Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes.

Keywords: Change-point; Locally; stationary; Frequency; domain; Kullback-Leibler (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (5)

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