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

In fnets: Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

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
)

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+).

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

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
)

fnets documentation built on May 29, 2024, 8:42 a.m.