fnets: Factor-adjusted network estimation

In Dom-Owens-UoB/fnets: Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Factor-adjusted network estimation

Description

Under a factor-adjusted vector autoregressive (VAR) model, the function estimates the spectral density and autocovariance matrices of the factor-driven common component and the idiosyncratic VAR process, the impulse response functions and common shocks for the common component, and VAR parameters, innovation covariance matrix and long-run partial correlations for the idiosyncratic component.

Usage

fnets(
  x,
  center = TRUE,
  fm.restricted = FALSE,
  q = c("ic", "er"),
  ic.op = NULL,
  kern.bw = NULL,
  common.args = list(factor.var.order = NULL, max.var.order = NULL, trunc.lags = 20,
    n.perm = 10),
  var.order = 1,
  var.method = c("lasso", "ds"),
  var.args = list(lambda = NULL, n.iter = NULL, n.cores = 1),
  do.threshold = FALSE,
  do.lrpc = FALSE,
  lrpc.adaptive = FALSE,
  tuning.args = list(tuning = c("cv", "bic"), n.folds = 1, penalty = NULL, path.length =
    10),
  robust = FALSE,
  robust.standardise = TRUE
)

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
fm.restricted	boolean; whether to estimate a restricted factor model using static PCA
q	Either the number of factors or a string specifying the factor number selection method; possible values are: "ic" information criteria-based methods of Alessi, Barigozzi & Capasso (2010) when fm.restricted = TRUE or Hallin and Liška (2007) when fm.restricted = FALSE "er" eigenvalue ratio of Ahn and Horenstein (2013) when fm.restricted = TRUE or Avarucci et al. (2022) when fm.restricted = FALSE see factor.number.
ic.op	choice of the information criterion penalty, see factor.number for further details
kern.bw	a positive integer specifying the kernel bandwidth for dynamic PCA; by default, it is set to floor(4 *(dim(x)[2]/log(dim(x)[2]))^(1/3))). When fm.restricted = TRUE, it is used to compute the number of lags for which autocovariance matrices are estimated
common.args	a list specifying the tuning parameters required for estimating the impulse response functions and common shocks. It contains: factor.var.order order of the blockwise VAR representation of the common component. If factor.var.order = NULL, it is selected blockwise by Schwarz criterion max.var.order maximum blockwise VAR order for the Schwarz criterion trunc.lags truncation lag for impulse response function estimation n.perm number of cross-sectional permutations involved in impulse response function estimation
var.order	order of the idiosyncratic VAR process; if a vector of integers is supplied, the order is chosen via tuning
var.method	a string specifying the method to be adopted for idiosyncratic VAR process estimation; possible values are: "lasso" Lasso-type l1-regularised M-estimation "ds" Dantzig Selector-type constrained l1-minimisation
var.args	a list specifying the tuning parameters required for estimating the idiosyncratic VAR process. It contains: lambda user-specified lambda; if lambda = NULL, it is chosen using the set tuning method specified in tuning.args; currently works only when length(var.order) = 1 n.iter maximum number of descent steps, by default depends on var.order; applicable when var.method = "lasso" n.cores number of cores to use for parallel computing, see makePSOCKcluster; applicable when var.method = "ds"
do.threshold	boolean; whether to perform adaptive thresholding of all parameter estimators with threshold
do.lrpc	boolean; whether to estimate the long-run partial correlation matrix or not
lrpc.adaptive	boolean; whether to use the adaptive estimation procedure
tuning.args	a list specifying arguments for selecting the tuning parameters involved in VAR parameter and (long-run) partial correlation matrix estimation. 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
robust	boolean; whether to truncate the data or not.
robust.standardise	boolean; whether to scale up the truncation parameter for each series by the MAD of the corresponding series or not.

Details

See Barigozzi, Cho and Owens (2024) for further details. List arguments do not need to be specified with all list components; any missing entries will be filled in with the default argument.

Value

an S3 object of class fnets, which contains the following fields:

q	number of factors
spec	if fm.restricted = FALSE a list containing estimates of the spectral density matrices for x, common and idiosyncratic components
acv	a list containing estimates of the autocovariance matrices for x, common and idiosyncratic components
loadings	if fm.restricted = TRUE, factor loadings; if fm.restricted = FALSE and q >= 1, a list containing estimators of the impulse response functions (as an array of dimension (p, q, trunc.lags + 2))
factors	if fm.restricted = TRUE, factor series; else, common shocks (an array of dimension (q, n))
idio.var	a list containing 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 regularisation parameter var.order VAR order
lrpc	see the output of par.lrpc
mean.x	if center = TRUE, returns a vector containing row-wise sample means of x; if center = FALSE, returns a vector of zeros
var.method	input parameter
do.lrpc	input parameter
kern.bw	input parameter

References

Ahn, S. C. & Horenstein, A. R. (2013) Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203–1227.

Alessi, L., Barigozzi, M., & Capasso, M. (2010) Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24):1806–1813.

Avarucci, M., Cavicchioli, M., Forni, M., & Zaffaroni, P. (2022) The main business cycle shock(s): Frequency-band estimation of the number of dynamic factors.

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).

Hallin, M. & Liška, R. (2007) Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association, 102(478), 603–617.

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).

See Also

predict.fnets, plot.fnets, print.fnets

Examples

out <- fnets(data.unrestricted,
  do.threshold = TRUE,
  var.args = list(n.cores = 2)
)
pre <- predict(out, common.method = "unrestricted")
plot(out, type = "granger", display = "network")
plot(out, type = "lrpc", display = "heatmap")

Dom-Owens-UoB/fnets documentation built on Nov. 22, 2024, 7:09 a.m.