Moving sum procedure for multiple change point detection in large factor models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913954317467648 |
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| author | Barigozzi, Matteo Cho, Haeran Trapani, Lorenzo |
| author_facet | Barigozzi, Matteo Cho, Haeran Trapani, Lorenzo |
| contents | This paper proposes a moving sum methodology for detecting multiple change points in high-dimensional time series under a factor model, where changes are attributed to those in loadings as well as emergence or disappearance of factors. We establish the asymptotic null distribution of the proposed test for family-wise error control, and show the consistency of the procedure for multiple change point estimation. Simulation studies and an application to a large dataset of volatilities demonstrate the competitive performance of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02918 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Moving sum procedure for multiple change point detection in large factor models Barigozzi, Matteo Cho, Haeran Trapani, Lorenzo Methodology This paper proposes a moving sum methodology for detecting multiple change points in high-dimensional time series under a factor model, where changes are attributed to those in loadings as well as emergence or disappearance of factors. We establish the asymptotic null distribution of the proposed test for family-wise error control, and show the consistency of the procedure for multiple change point estimation. Simulation studies and an application to a large dataset of volatilities demonstrate the competitive performance of the proposed method. |
| title | Moving sum procedure for multiple change point detection in large factor models |
| topic | Methodology |
| url | https://arxiv.org/abs/2410.02918 |