Taxonomy and Estimation of Multiple Breakpoints in High-Dimensional Factor Models Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2503.06645
This paper investigates the estimation of high-dimensional factor models in which factor loadings undergo an unknown number of structural changes over time. Given that a model with multiple changes in factor loadings can be observationally indistinguishable from one with constant loadings but varying factor variances, this reduces the high-dimensional structural change problem to a lower-dimensional one. Due to the presence of multiple breakpoints, the factor space may expand, potentially causing the pseudo factor covariance matrix within some regimes to be singular. We define two types of breakpoints: {\bf a singular change}, where the number of factors in the combined regime exceeds the minimum number of factors in the two separate regimes, and {\bf a rotational change}, where the number of factors in the combined regime equals that in each separate regime. Under a singular change, we derive the properties of the small eigenvalues and establish the consistency of the QML estimators. Under a rotational change, unlike in the single-breakpoint case, the pseudo factor covariance matrix within each regime can be either full rank or singular, yet the QML estimation error for the breakpoints remains stably bounded. We further propose an information criterion (IC) to estimate the number of breakpoints and show that, with probability approaching one, it accurately identifies the true number of structural changes. Monte Carlo simulations confirm strong finite-sample performance. Finally, we apply our method to the FRED-MD dataset, identifying five structural breaks in factor loadings between 1959 and 2024.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2503.06645
- https://arxiv.org/pdf/2503.06645
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4416679364
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4416679364Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2503.06645Digital Object Identifier
- Title
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Taxonomy and Estimation of Multiple Breakpoints in High-Dimensional Factor ModelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-09Full publication date if available
- Authors
-
Jushan Bai, Xu HanList of authors in order
- Landing page
-
https://arxiv.org/abs/2503.06645Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2503.06645Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2503.06645Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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