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Heteroscedasticity and Autocorrelation Robust Structural Change Detection

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Inference for Change-Point and Related Processes

The assumption of (weak) stationarity is crucial for the validity of most of the conventional tests of structure change in time series. Under complicated non-stationary temporal dynamics, we argue that traditional testing procedures result in mixed structural change signals of the first and second order and hence could lead to biased testing results. We propose a simple and unied bootstrap testing procedure which provides consistent testing results under general forms of smooth and abrupt changes in the temporal dynamics of the time series. Monte Carlo experiments are performed to compare our testing procedure to various traditional tests. Our robust bootstrap test is applied to testing changes in an environmental time series and our procedure is shown to provide more reliable results than the conventional tests.

This talk is part of the Isaac Newton Institute Seminar Series series.

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