Heteroscedasticity and Autocorrelation Robust Structural Change Detection

• Activity Number: 616
• Type: Invited
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Zhou Zhou*+
• Companies: University of Toronto
• Keywords: Piecewise locally stationary time series ; structural change detection ; bootstrap

Abstract

The assumption of (weak) stationarity is crucial for the validity of most of the conventional tests of structure change in time series. In view of the increasing empirical evidences of complicated non-stationary temporal dynamics of time series collected from various fields such as climatology, economics and signal processing, we argue that traditional testing procedures result in mixed structural change signals of the first and second order and hence could lead to seriously biased testing results. We propose a simple and unified bootstrap testing procedure which provides consistent testing results under very 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 and a financial time series and our procedure is shown to provide more reliable results than the conventional tests.