Lei Jia, Shouri Hu, Zhaoxing Gao
arXiv 1 Jun 2026 · Statistics — Methodology
arXiv:2606.01553 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a canonical-correlation-based method for detecting structural changes in high-dimensional transformed factor models. The proposed approach exploits the low-rank canonical-correlation structure induced by dynamically dependent common factors, while serially uncorrelated idiosyncratic components correspond to a noise subspace with zero canonical correlations. We construct an eigenvalue-ratio criterion that measures residual dynamic dependence in the estimated noise subspace and identifies the true change point under sufficient separation of the regime-specific loading spaces or dynamic canonical correlation structures. Since the change-point location and the regime-specific factor numbers are both unknown, we further propose an alternating iterative estimation procedure that updates them sequentially until convergence. Under suitable mixing and moment conditions, we establish asymptotic properties of the proposed estimators, with convergence rates depending explicitly on factor strength, cross-sectional dimension, and sample size. Monte Carlo experiments and empirical applications to intraday stock returns and U.S. temperature series demonstrate the finite-sample
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gao, Z., Tsay, R. S (2019) A Structural-Factor Approach to Modeling High-Dimensional Time Series and Space-Time Data self | 0.811 | 4 | 2 | 100% |
| 2 | Tiao, G. C., Tsay, R. S. (1989). Model specification in multivariate… Journal of the Royal Statistical Society: Series B (Statistical Methodology) 51: 157–213 | 0.737 | 3 | 2 | 100% |
| 3 | Lam, C., Yao, Q., Bathia, N (2011) Estimation of latent factors for high-dimensional time series | 0.644 | 3 | 2 | 67% |
| 4 | Liu, X., Chen, R (2020) Threshold factor models for high-dimensional time series | 0.644 | 2 | 2 | 100% |
| 5 | Liu, X., Zhang, T (2022) Estimating Change-Point Latent Factor Models for High-Dimensional Time Series | 0.644 | 2 | 2 | 100% |
| 6 | Xia, Q., Liang, R., Wu, J (2017) Transformed Contribution Ratio Test for the Number of Factors in Static Approximate Factor Models | 0.644 | 2 | 2 | 100% |
| 7 | Pan, J., Yao, Q (2008) Modelling multiple time series via common factors | 0.644 | 2 | 2 | 100% |
| 8 | Lam, C., Yao, Q (2012) Factor modeling for high-dimensional time series: inference for the number of factors | 0.585 | 3 | 1 | 100% |
| 9 | Liu, X., Chen, R (2016) Regime-switching factor models for high-dimensional time series | 0.511 | 2 | 1 | 100% |
| 10 | Bai, J., Han, X., Shi, Y (2020) Estimation and Inference of Change Points in High Dimensional Factor Models | 0.405 | 1 | 1 | 100% |
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