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

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

by Dom Owens, Haeran Cho and Matteo Barigozzi arXiv:2301.11675 accompanying the R package, and

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

by Matteo Barigozzi, Haeran Cho and Dom Owens arXiv:2201.06110 for details of the methodology.

To install `fnets`

from CRAN:

`install.packages("fnets")`

To install from GitHub:

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

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, var.order = 1, var.method = "lasso", do.lrpc = FALSE)`

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

Estimate the (long-run) partial correlation-based networks directly
using `fnets`

:

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

Forecast `n.ahead`

steps:

```
pr <- predict(out, n.ahead = 1, common.method = "restricted")
pr$forecast
```