fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling

Vector autoregressive (VAR) models are useful for modelling high-dimensional time series data. This paper introduces the package fnets, which implements the suite of methodologies proposed by (Barigozzi et al. 2023) for the network estimation and forecasting of high-dimensional time series under a factor-adjusted vector autoregressive model, which permits strong spatial and temporal correlations in the data. Additionally, we provide tools for visualising the networks underlying the time series data after adjusting for the presence of factors. The package also offers data-driven methods for selecting tuning parameters including the number of factors, the order of autoregression, and thresholds for estimating the edge sets of the networks of interest in time series analysis. We demonstrate various features of fnets on simulated datasets as well as real data on electricity prices.

Dom Owens (School of Mathematics, University of Bristol) , Haeran Cho (School of Mathematics, University of Bristol) , Matteo Barigozzi (Department of Economics, Università di Bologna)
2023-12-18

0.1 Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-070.zip

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For attribution, please cite this work as

Owens, et al., "fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling", The R Journal, 2023

BibTeX citation

@article{RJ-2023-070,
  author = {Owens, Dom and Cho, Haeran and Barigozzi, Matteo},
  title = {fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling},
  journal = {The R Journal},
  year = {2023},
  note = {https://doi.org/10.32614/RJ-2023-070},
  doi = {10.32614/RJ-2023-070},
  volume = {15},
  issue = {3},
  issn = {2073-4859},
  pages = {214-239}
}

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