Degui Li, Yuying Sun, Boyao Wu
arXiv 24 Jun 2026 · Econometrics · publishedJournal of Econometrics (2020) · 45 citations (OpenAlex)
arXiv:2606.25292 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we introduce a flexible time-varying multi-layer network vector autoregression (VAR) model framework for large-scale time series, allowing agents in dynamic systems to interact through multiple channels and incorporating multiple adjacency matrices to capture network spillover effects. We propose a penalized model averaging method to determine a time-varying optimal combination of multi-layer network VAR candidate models whose number may be divergent. Under some regularity conditions, the asymptotic properties such as asymptotic optimality and convergence rates of the proposed time-varying weight estimation are derived in the contexts of both the in-sample fitting and out-of-sample prediction. In addition, we extend the conformal prediction method to construct prediction bands for locally stationary time series. Monte-Carlo simulation studies and an empirical application to forecast CPI inflation by combining multiple network information are given to illustrate reliable finite-sample estimation and predictive performance of the developed methodology.
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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 | Li et al (2026) Estimation of grouped time-varying network vector autoregressive models self | 1.000 | 5 | 3 | 100% |
| 2 | Sun et al (2023) Penalized time-varying model averaging self | 0.928 | 4 | 3 | 100% |
| 3 | Zhu et al (2017) Network vector autoregression | 0.811 | 4 | 2 | 100% |
| 4 | Diebold and Yilmaz (2014) On the network topology of variance decompositions: Measuring the connectedness of financial firms | 0.737 | 5 | 3 | 40% |
| 5 | Chen, Hong and Li (2024) Time-varying forecast combination for factor-augmented regressions with smooth structural changes self | 0.644 | 2 | 2 | 100% |
| 6 | Sun, Chen and Gao (2025) Model averaging for time-varying vector autoregressions | 0.644 | 2 | 2 | 100% |
| 7 | Tu and Wang (2025) Quantile prediction with factor-augmented regression: Structural instability and model uncertainty | 0.644 | 2 | 2 | 100% |
| 8 | Amiti, Redding and Weinstein (2019) The impact of the 2018 tariffs on prices and welfare | 0.511 | 2 | 1 | 100% |
| 9 | Chen et al (2025) Estimating time-varying networks for high-dimensional time series | 0.511 | 2 | 1 | 100% |
| 10 | Ciccarelli and Mojon (2010) Global inflation | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 61 scored citations.