fnets (version 0.1.6)

fnets.var: l1-regularised Yule-Walker estimation for VAR processes

Description

Estimates the VAR parameter matrices via l1-regularised Yule-Walker estimation and innovation covariance matrix via constrained l1-minimisation.

Usage

fnets.var(
  x,
  center = TRUE,
  method = c("lasso", "ds"),
  lambda = NULL,
  var.order = 1,
  tuning.args = list(tuning = c("cv", "bic"), n.folds = 1, penalty = NULL, path.length =
    10),
  do.threshold = FALSE,
  n.iter = NULL,
  tol = 0,
  n.cores = 1
)

Value

a list which contains the following fields:

beta

estimate of VAR parameter matrix; each column contains parameter estimates for the regression model for a given variable

Gamma

estimate of the innovation covariance matrix

lambda

l1-regularisation parameter

var.order

VAR order

mean.x

if center = TRUE, returns a vector containing row-wise sample means of x; if center = FALSE, returns a vector of zeros

Arguments

x

input time series each column representing a time series variable; it is coerced into a ts object

center

whether to de-mean the input x

method

a string specifying the method to be adopted for VAR process estimation; possible values are:

"lasso"

Lasso-type l1-regularised M-estimation

"ds"

Dantzig Selector-type constrained l1-minimisation

lambda

l1-regularisation parameter; if lambda = NULL, tuning is employed to select the parameter

var.order

order of the VAR process; if a vector of integers is supplied, the order is chosen via tuning

tuning.args

a list specifying arguments for tuning for selecting the regularisation parameter (and VAR order). It contains:

tuning

a string specifying the selection procedure for var.order and lambda; possible values are: "cv" for cross validation, and "bic" for information criterion

n.folds

if tuning = "cv", positive integer number of folds

penalty

if tuning = "bic", penalty multiplier between 0 and 1; if penalty = NULL, it is set to 1/(1+exp(dim(x)[1])/dim(x)[2]))

by default

path.length

positive integer number of regularisation parameter values to consider; a sequence is generated automatically based in this value

do.threshold

whether to perform adaptive thresholding of VAR parameter estimator with threshold

n.iter

maximum number of descent steps, by default depends on var.order; applicable when method = "lasso"

tol

numerical tolerance for increases in the loss function; applicable when method = "lasso"

n.cores

number of cores to use for parallel computing, see makePSOCKcluster; applicable when method = "ds"

Details

Further information can be found in Barigozzi, Cho and Owens (2024+).

References

Barigozzi, M., Cho, H. & Owens, D. (2024+) FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series. Journal of Business & Economic Statistics (to appear).

Owens, D., Cho, H. & Barigozzi, M. (2024+) fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling. The R Journal (to appear).

Examples

library(fnets)

set.seed(123)
n <- 500
p <- 50
idio <- sim.var(n, p)
x <- idio$data

fv <- fnets.var(x,
  n.cores = 2
)