Journal
JOURNAL OF TIME SERIES ANALYSIS
Volume 34, Issue 1, Pages 1-16Publisher
WILEY
DOI: 10.1111/j.1467-9892.2012.00819.x
Keywords
Change-points; CUSUM; long memory; mean change; unit-root; variance change
Funding
- NSF [DMS 0905400]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0905400] Funding Source: National Science Foundation
Ask authors/readers for more resources
This paper gives an account of some of the recent work on structural breaks in time series models. In particular, we show how procedures based on the popular cumulative sum, CUSUM, statistics can be modified to work also for data exhibiting serial dependence. Both structural breaks in the unconditional and conditional mean as well as in the variance and covariance/correlation structure are covered. CUSUM procedures are nonparametric by design. If the data allows for parametric modeling, we demonstrate how likelihood approaches may be utilized to recover structural breaks. The estimation of multiple structural breaks is discussed. Furthermore, we cover how one can disentangle structural breaks (in the mean and/or the variance) on one hand and long memory or unit roots on the other. Several new lines of research are briefly mentioned.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available