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

School of Mathematics, University of Bristol

domowens1@gmail.com

School of Mathematics, University of Bristol

haeran.cho@bristol.ac.uk

Department of Economics, Università di Bologna

matteo.barigozzi@unibo.it

Abstract

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, Cho, and Owens 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.

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