Degui Li, Bin Peng, Songqiao Tang, Weibiao Wu
arXiv 17 Mar 2023 · Statistics — Methodology · 2 citations (OpenAlex)
arXiv:2303.10117 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with nonparametrically estimated distance matrix is combined with a ratio criterion to consistently estimate the latent group number and membership. A post-grouping local linear smoothing method is proposed to estimate the group-specific time-varying momentum and network effects, substantially improving the convergence rates of the preliminary estimates which ignore the latent structure. We further modify the methodology and theory to allow for structural breaks in either the group membership, group number or group-specific coefficient functions. Numerical studies including Monte-Carlo simulation and an empirical application are presented to examine the finite-sample performance of the developed model and methodology.
appendix boundary found by appendix_command · 42% 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 | Yin, Safikhani \ Michailidis (2024) A functional coefficients network autoregressive model | 1.000 | 7 | 5 | 100% |
| 2 | Zhu et al (2017) Network vector autoregression | 1.000 | 5 | 3 | 100% |
| 3 | Li et al (2024) Inference of grouped time-varying network vector autoregression models self | 0.965 | 10 | 6 | 90% |
| 4 | Zhu, Xu \ Fan (2023) Simultaneous estimation and group identification for network vector autoregressive model with heterogeneous nodes | 0.935 | 11 | 5 | 82% |
| 5 | Chen, Fan \ Zhu (2023) Community network autoregression for high-dimensional time series | 0.737 | 3 | 2 | 100% |
| 6 | Chen (2019) Estimating latent group structure in time-varying coefficient panel data models | 0.737 | 3 | 2 | 100% |
| 7 | Fan \ Gijbels (1996) Local Polynomial Modelling and Its Applications | 0.737 | 3 | 2 | 100% |
| 8 | Vogt \ Linton (2020) Multiscale clustering of nonparametric regression curves | 0.737 | 3 | 2 | 100% |
| 9 | Wu (2019) Time-varying network vector autoregression model self | 0.737 | 3 | 2 | 100% |
| 10 | Chen et al (2023) Estimating time-varying networks for high-dimensional time series | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 53 scored citations.