Degui Li, Runze Li, Han Lin Shang
arXiv 14 Apr 2023 · Statistics — Methodology · publishedThe Annals of Statistics (2024) · 5 citations (OpenAlex)
arXiv:2304.07003 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we consider detecting and estimating breaks in heterogeneous mean functions of high-dimensional functional time series which are allowed to be cross-sectionally correlated and temporally dependent. A new test statistic combining the functional CUSUM statistic and power enhancement component is proposed with asymptotic null distribution theory comparable to the conventional CUSUM theory derived for a single functional time series. In particular, the extra power enhancement component enlarges the region where the proposed test has power, and results in stable power performance when breaks are sparse in the alternative hypothesis. Furthermore, we impose a latent group structure on the subjects with heterogeneous break points and introduce an easy-to-implement clustering algorithm with an information criterion to consistently estimate the unknown group number and membership. The estimated group structure can subsequently improve the convergence property of the post-clustering break point estimate. Monte-Carlo simulation studies and empirical applications show that the proposed estimation and testing techniques have satisfactory performance in finite samples.
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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 | Aue, Rice and Sönmez (2018) Detecting and dating structural breaks in functional data without dimension reduction | 1.000 | 9 | 4 | 100% |
| 2 | Horváth, Kokoszka and Rice (2014) Testing stationarity of functional time series | 0.811 | 4 | 2 | 100% |
| 3 | Bosq (2000) Linear Processes in Function Spaces | 0.737 | 3 | 3 | 67% |
| 4 | Bai (2010) Common breaks in means and variances for panel data | 0.737 | 3 | 2 | 100% |
| 5 | Fan, Liao \ Yao (2015) Power enhancement in high-dimensional cross-sectional tests | 0.737 | 3 | 2 | 100% |
| 6 | Sharipov, Tewes and Wendler (2016) Sequential block bootstrap in a Hilbert space with application to change point analysis | 0.737 | 3 | 2 | 100% |
| 7 | Li, Robinson and Shang (2023) Nonstationary fractionally integrated functional time series self | 0.644 | 2 | 2 | 100% |
| 8 | Horváth and Husková (2012) Change-point detection in panel data | 0.511 | 2 | 2 | 50% |
| 9 | Aue et al (2009) Estimation of a change-point in the mean function of functional data | 0.405 | 1 | 1 | 100% |
| 10 | Aston and Kirch (2012) Detecting and estimating changes in dependent functional data | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 39 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $$-norm | 0.405 | 1 | 1 |