Inference of Breakpoints in High-dimensional Time Series Article Swipe
Likai Chen
,
Ning Wang
,
Wei Biao Wu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/01621459.2021.1893178
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/01621459.2021.1893178
For multiple change-points detection of high-dimensional time series, we provide asymptotic theory concerning the consistency and the asymptotic distribution of the breakpoint statistics and estimated break sizes. The theory backs up a simple two-step procedure for detecting and estimating multiple change-points. The proposed two-step procedure involves the maximum of a MOSUM (moving sum) type statistics in the first step and a CUSUM (cumulative sum) refinement step on an aggregated time series in the second step. Thus, for a fixed time-point, we can capture both the biggest break across different coordinates and aggregating simultaneous breaks over multiple coordinates. Extending the existing high-dimensional Gaussian approximation theorem to dependent data with jumps, the theory allows us to characterize the size and power of our multiple change-point test asymptotically. Moreover, we can make inferences on the breakpoints estimates when the break sizes are small. Our theoretical setup incorporates both weak temporal and strong or weak cross-sectional dependence and is suitable for heavy-tailed innovations. A robust long-run covariance matrix estimation is proposed, which can be of independent interest. An application on detecting structural changes of the U.S. unemployment rate is considered to illustrate the usefulness of our method.
Related Topics
Concepts
Series (stratigraphy)
CUSUM
Asymptotic analysis
Mathematics
Gaussian
Covariance
Applied mathematics
Asymptotic distribution
Inference
Consistency (knowledge bases)
Statistics
Econometrics
Algorithm
Computer science
Estimator
Discrete mathematics
Artificial intelligence
Paleontology
Biology
Quantum mechanics
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1080/01621459.2021.1893178
- OA Status
- green
- References
- 37
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3144740445
All OpenAlex metadata
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https://openalex.org/W3144740445Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/01621459.2021.1893178Digital Object Identifier
- Title
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Inference of Breakpoints in High-dimensional Time SeriesWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-04-08Full publication date if available
- Authors
-
Likai Chen, Ning Wang, Wei Biao WuList of authors in order
- Landing page
-
https://doi.org/10.1080/01621459.2021.1893178Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://figshare.com/articles/journal_contribution/Inference_of_Breakpoints_in_High-dimensional_Time_Series/14818172Direct OA link when available
- Concepts
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Series (stratigraphy), CUSUM, Asymptotic analysis, Mathematics, Gaussian, Covariance, Applied mathematics, Asymptotic distribution, Inference, Consistency (knowledge bases), Statistics, Econometrics, Algorithm, Computer science, Estimator, Discrete mathematics, Artificial intelligence, Paleontology, Biology, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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37Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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