Jia Chen, Degui Li, Yuning Li, Oliver Linton
arXiv 5 Feb 2023 · Statistics — Methodology · publishedJournal of Econometrics (2025) · 7 citations (OpenAlex)
arXiv:2302.02476 · PDF · DOI · OpenAlex · Extracted main text
We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear method with time-varying weighted group LASSO to jointly estimate the transition matrices and identify their significant entries, and a time-varying CLIME method to estimate the precision matrices. The estimated transition and precision matrices are then used to determine the time-varying network structures. Under some mild conditions, we derive the theoretical properties of the proposed estimates including the consistency and oracle properties. In addition, we extend the methodology and theory to cover highly-correlated large-scale time series, for which the sparsity assumption becomes invalid and we allow for common factors before estimating the factor-adjusted time-varying networks. We provide extensive simulation studies and an empirical application to a large U.S. macroeconomic dataset to illustrate the finite-sample performance of our methods.
appendix boundary found by appendix_command · 51% 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 | Basu \ Michailidis (2015) Regularized estimation in sparse high-dimensional time series models | 1.000 | 5 | 3 | 100% |
| 2 | Su \ Wang (2017) On time-varying factor models: estimation and testing | 0.941 | 6 | 5 | 83% |
| 3 | Li, Ke and Zhang (2015) Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models | 0.874 | 9 | 6 | 67% |
| 4 | Cai, Liu \ Luo (2011) A constrained $_1$ minimization approach to sparse precision matrix estimation | 0.843 | 5 | 4 | 60% |
| 5 | Barigozzi \ Brownlees (2019) NETS: Network estimation for time series | 0.811 | 4 | 2 | 100% |
| 6 | Kock \ Callot (2015) Oracle inequalities for high dimensional vector autoregressions | 0.794 | 6 | 3 | 50% |
| 7 | Bai \ Ng (2002) Determining the number of factors in approximate factor models | 0.737 | 3 | 3 | 67% |
| 8 | Lian (2012) Variable selection for high-dimensional generalized varying-coefficient models | 0.737 | 3 | 3 | 67% |
| 9 | Miao, Phillips \ Su (2022) High-dimensional VARs with common factors | 0.737 | 3 | 3 | 67% |
| 10 | Ding, Qiu \ Chen (2017) Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 77 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.