The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Structural breaks in time series
Open accessAbstract
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.
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Cites
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Metadata
- Authors
- Aue, A., & Horváth, L.
- Venue
- Journal of Time Series Analysis
- Publisher
- Wiley
- Published
- 2012-09-14
- Volume
- 34
- Issue
- 1
- Pages
- 1-16
- ISSN
- 0143-9782 (ISSN-L)
- 1467-9892
- OpenAlex
- W1544178588
- Cited by
- 443
- References
- 82
- FWCI
- 41.12
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
