Heteroscedasticity and Autocorrelation Robust Structural Change Detection

Abstract

The assumption of (weak) stationarity is crucial for the validity of most of the conventional tests of structure change in time series. Under complicated nonstationary 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. The article proposes a simple and unified bootstrap testing procedure that 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 with various traditional tests. Our robust bootstrap test is applied to testing changes in an environmental and a financial time series and our procedure is shown to provide more reliable results than the conventional tests.

KEY WORDS:

• Bootstrap
• Climate change
• CUSUM
• Piecewise locally stationary time series

Acknowledgments

The author is grateful to the editor, the associate editor, and two referees for their many helpful comments. The research was supported in part by NSERC of Canada.