Heteroscedasticity and Autocorrelation Robust Structural Change Detection

• 成果类型: Article
• 署名作者: Zhou, Zhou
• 署名单位: University of Toronto
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.787184
• 发表日期: 2013
• 页码: 726-740
• 关键词: change-points; heteroskedasticity; regression; temperature; variability; squares; models; TRENDS; ORDER

摘要

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.