# fnets

Contains methods for network estimation and forecasting for high-dimensional time series under a factor-adjusted VAR model. See

FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series

by Matteo Barigozzi, Haeran Cho and Dom Owens arXiv:2201.06110 for full details.

## Installation

To install `fnets` from GitHub:

``devtools::install_github("https://github.com/Dom-Owens-UoB/fnets")``

## Usage

We can generate an example dataset used in the above paper for simulation studies, by separately generating the factor-driven common component and the idiosyncratic VAR process as

``````set.seed(123)
n <- 500
p <- 50
common <- sim.unrestricted(n, p)
idio <- sim.var(n, p)
x <- common\$data + idio\$data``````

Fit a factor-adjusted VAR model with `q = 2` factors and `lasso` for VAR transition matrix estimation

``out <- fnets(x, q = 2, idio.var.order = 1, idio.method = "lasso", lrpc.method = "none")``

Plot the Granger network induced by the estimated VAR transition matrices:

``plot(out, type = "granger", display = "network")``

Estimate and plot the partial-correlation and long-run partial correlation-based networks:

``````plrpc <- par.lrpc(out, x)
out\$lrpc <- plrpc
out\$lrpc.method <- 'par'
plot(out, type = "lrpc", display = "heatmap")``````

Of course, we can estimate the (long-run) partial correlation-based networks directly using `fnets`:

``out <- fnets(x, q = 2, idio.var.order = 1, idio.method = "lasso", lrpc.method = "par")``

``````pr <- predict(out, x, h = 1, common.method = "restricted")