Younghoon Kim, Changryong Baek
arXiv 14 Apr 2026 · Statistics — Methodology
arXiv:2604.12563 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a dynamic network framework for uncovering latent community paths in high-dimensional VAR-type models. By embedding a degree-corrected stochastic co-blockmodel (ScBM) into the transition matrices of VAR-type systems, we separate sending and receiving roles at the node level and summarize complex directional dependence in an interpretable low-dimensional form. Our method integrates directed spectral co-clustering with eigenvector smoothing to track how directional groups split, merge, or persist over time. This framework accommodates both periodic VAR (PVAR) models for cyclical seasonal evolution and generalized VHAR models for structural transitions across ordered dependence horizons. We establish non-asymptotic misclassification bounds for both procedures and provide supporting evidence through Monte Carlo experiments. Applications to U.S.\ nonfarm payrolls distinguish a recurrent business-centered core from more mobile, seasonally sensitive sectors. In global stock volatilities, the results reveal a compact U.S.-centered long-horizon block, a Europe-heavy developed core, and a more dynamic short-horizon reallocation of peripheral and bridge markets.
appendix boundary found by appendix_command · 55% of the source is main text. Read the extracted text to check this.
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 | Rohe, K., Qin, T., and Yu, B (2016) Co-clustering directed graphs to discover asymmetries and directional communities | 0.941 | 6 | 5 | 83% |
| 2 | Baek, C. and Park, M (2021) Sparse vector heterogeneous autoregressive modeling for realized volatility self | 0.928 | 5 | 5 | 80% |
| 3 | Qin, T. and Rohe, K (2013) Regularized spectral clustering under the degree-corrected stochastic blockmodel | 0.928 | 5 | 4 | 80% |
| 4 | Liu, F., Choi, D., Xie, L., and Roeder, K (2018) Global spectral clustering in dynamic networks | 0.928 | 5 | 3 | 80% |
| 5 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.928 | 4 | 3 | 100% |
| 6 | Basu, S. and Michailidis, G (2015) Regularized estimation in sparse high-dimensional time series models | 0.863 | 14 | 4 | 64% |
| 7 | Ursu, E. and Duchesne, P (2009) On modelling and diagnostic checking of vector periodic autoregressive time series models | 0.843 | 3 | 3 | 100% |
| 8 | Gumundsson, G. S. and Brownlees, C (2021) Detecting groups in large vector autoregressions | 0.769 | 11 | 5 | 45% |
| 9 | Baek, C., Davis, R. A., and Pipiras, V (2018) Periodic dynamic factor models: Estimation approaches and applications self | 0.644 | 2 | 2 | 100% |
| 10 | Wang, Z., Liang, Y., and Ji, P (2020) Spectral algorithms for community detection in directed networks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.