Bartlett weights

Description

Bartlett weights

Usage

Bartlett.weights(x)

Factor number estimator of Alessi, Barigozzi and Capasso (2010)

Description

Estimates the number of factors by minimising an information criterion over sub-samples of the data. Currently the three information criteria proposed in Alessi, Barigozzi and Capasso (2010) (ic.op = 1, 2, 3) and their variations with logarithm taken on the cost (ic.op = 4, 5, 6) are implemented, with ic.op = 5 recommended as a default choice based on numerical experiments.

Usage

abc.factor.number(x, covx = NULL, q.max = NULL, center = TRUE)

Arguments

Details

See Alessi, Barigozzi and Capasso (2010) for further details.

Value

a list containing

References

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.

Blockwise VAR estimation under GDFM

Description

Blockwise VAR estimation under GDFM

Usage

common.irf.estimation(
  xx,
  Gamma_c,
  q,
  factor.var.order = NULL,
  max.var.order = NULL,
  trunc.lags,
  n.perm
)

Forecasting the factor-driven common component

Description

Produces forecasts of the common component for a given forecasting horizon by estimating the best linear predictors

Usage

common.predict(object, x, n.ahead = 1, fc.restricted = TRUE, r = c("ic", "er"))

Arguments

Value

a list containing

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

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).

Forni, M., Hallin, M., Lippi, M. & Reichlin, L. (2005) The generalized dynamic factor model: one-sided estimation and forecasting. Journal of the American Statistical Association, 100(471), 830–840.

Forni, M., Hallin, M., Lippi, M. & Zaffaroni, P. (2017) Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis. Journal of Econometrics, 199(1), 74–92.

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

## Not run: 
out <- fnets(data.unrestricted, q = NULL, var.order = 1, var.method = "lasso",
do.lrpc = FALSE, var.args = list(n.cores = 2))
cpre <- common.predict(out)
ipre <- idio.predict(out, cpre)

## End(Not run)

Simulated data from the restricted factor-adjusted vector autoregression model

Description

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

Usage

data.restricted

Format

## 'data.restricted' A ts object with 500 rows (observations) and 50 columns (series)

Simulated data from the unrestricted factor-adjusted vector autoregression model

Description

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

Usage

data.unrestricted

Format

## 'data.unrestricted' A ts object with 500 rows (observations) and 50 columns (series)

Dynamic PCA

Description

Performs principal components analysis in frequency domain for identifying common and idiosyncratic components.

Usage

dyn.pca(
  xx,
  q = NULL,
  q.method = c("ic", "er"),
  ic.op = 5,
  kern.bw = NULL,
  mm = NULL
)

Arguments

Value

a list containing

extended Bayesian Information Criterion

Description

extended Bayesian Information Criterion

Usage

ebic(object, n, penalty = 0)

full likelihood

Description

full likelihood

Usage

f.func.full(GG, gg, A)

Factor number selection methods

Description

Methods to estimate the number of factor. When method = 'er', the factor number is estimated by maximising the ration of successive eigenvalues. When method = 'ic', the information criterion-methods discussed in Hallin and Liška (2007) (when fm.restricted = FALSE) and Alessi, Barigozzi and Capasso (2010) (when fm.restricted = TRUE) are implemented. The information criterion called by ic.op = 5 (as an argument to fnets or fnets.factor.model) is recommended by default.

Usage

factor.number(
  x,
  fm.restricted = FALSE,
  method = c("ic", "er"),
  q.max = NULL,
  center = TRUE
)

Arguments

Details

For further details, see references.

Value

S3 object of class factor.number. If method = "ic", a vector containing minimisers of the six information criteria, otherwise, the maximiser of the eigenvalue ratio

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., and 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.

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

plot.factor.number, print.factor.number

Examples

library(fnets)
## Alessi, Barigozzi, and Capasso method for restricted models
abc <- factor.number(data.restricted, fm.restricted = TRUE)
print(abc)
plot(abc)

## Eigenvalue ratio method
er <- factor.number(data.restricted, method = "er", fm.restricted = TRUE)
print(er)
plot(er)

## Hallin and Liška method for unrestricted models
hl <- factor.number(data.unrestricted, fm.restricted = FALSE)
print(hl)
plot(hl)

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(n.iter = NULL, n.cores = 1),
  do.threshold = FALSE,
  do.lrpc = TRUE,
  lrpc.adaptive = FALSE,
  tuning.args = list(tuning = c("cv", "bic"), n.folds = 1, penalty = NULL, path.length =
    10)
)

Arguments

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:

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")

Factor model estimation

Description

Performs factor modelling under either restricted (static) or unrestricted (dynamic) factor models

Usage

fnets.factor.model(
  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)
)

Arguments

Details

See Barigozzi, Cho and Owens (2024+) for further details.

Value

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

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

print.fm, predict.fm

Examples

out <- fnets.factor.model(data.restricted, fm.restricted = TRUE)

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

Details

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

Value

a list which contains the following fields:

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
)

internal function for fnets.var

Description

internal function for fnets.var

Usage

fnets.var.internal(
  xx,
  acv,
  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
)

Factor number estimator of Hallin and Liška (2007)

Description

Estimates the number of factors by minimising an information criterion over sub-samples of the data. Currently the three information criteria proposed in Hallin and Liška (2007) (ic.op = 1, 2 or 3) and their variations with logarithm taken on the cost (ic.op = 4, 5 or 6) are implemented, with ic.op = 5 recommended as a default choice based on numerical experiments.

Usage

hl.factor.number(x, q.max = NULL, mm = NULL, center = TRUE)

Arguments

Details

See Hallin and Liška (2007) for further details.

Value

a list containing

References

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.

Forecasting idiosyncratic VAR process

Description

Produces forecasts of the idiosyncratic VAR process for a given forecasting horizon by estimating the best linear predictors

Usage

idio.predict(object, x, cpre, n.ahead = 1)

Arguments

Value

a list containing

Examples

## Not run: 
out <- fnets(data.unrestricted,
do.lrpc = FALSE, var.args = list(n.cores = 2))
cpre <- common.predict(out)
ipre <- idio.predict(out, cpre)

## End(Not run)

logarithmic factorial of 'n'

Description

logarithmic factorial of 'n'

Usage

logfactorial(n)

Convert networks into igraph objects

Description

Convert networks into igraph objects

Usage

network(object, ...)

Arguments

See Also

network.fnets

Convert networks estimated by fnets into igraph objects

Description

Converts S3 objects of class fnets into a network. Produces an igraph object for the three networks underlying factor-adjusted VAR processes: (i) directed network representing Granger causal linkages, as given by estimated VAR transition matrices summed across the lags, (ii) undirected network representing contemporaneous linkages after accounting for lead-lag dependence, as given by partial correlations of VAR innovations, (iii) undirected network summarising (i) and (ii) as given by long-run partial correlations of VAR processes. When plotting the network, note that the edge weights may be negative since they correspond to the entries of the estimators of VAR parameters and (long-run) partial correlations.

Usage

## S3 method for class 'fnets'
network(
  object,
  type = c("granger", "pc", "lrpc"),
  names = NA,
  groups = NA,
  group.colours = NA,
  ...
)

Arguments

Value

a list containing

See Also

fnets, plot.fnets

Examples

out <- fnets(data.unrestricted,
  do.threshold = TRUE,
  var.args = list(n.cores = 2)
)
net <- network(out, type = "granger")$network
plot(net, layout = igraph::layout_in_circle(net))
network(out, type = "pc")
network(out, type = "lrpc")

Parametric estimation of long-run partial correlations of factor-adjusted VAR processes

Description

Returns a parametric estimate of long-run partial correlations of the VAR process from the VAR parameter estimates and the inverse of innovation covariance matrix obtained via constrained l1-minimisation.

Usage

par.lrpc(
  object,
  eta = NULL,
  tuning.args = list(n.folds = 1, path.length = 10),
  lrpc.adaptive = FALSE,
  eta.adaptive = NULL,
  do.correct = TRUE,
  do.threshold = FALSE,
  n.cores = 1
)

Arguments

Details

See Barigozzi, Cho and Owens (2024+) for further details, and Cai, Liu and Zhou (2016) for further details on the adaptive estimation procedure.

Value

a list containing

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).

Cai, T. T., Liu, W., & Zhou, H. H. (2016) Estimating sparse precision matrix: Optimal rates of convergence and adaptive estimation. The Annals of Statistics, 44(2), 455-488.

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

out <- fnets(data.unrestricted, do.lrpc = FALSE, var.args = list(n.cores = 2))
plrpc <- par.lrpc(out, n.cores = 2)
out$lrpc <- plrpc
out$do.lrpc <- TRUE
plot(out, type = "pc", display = "network")
plot(out, type = "lrpc", display = "heatmap")

Plot factor number

Description

Plots the eigenvalue ratio or information criteria from a factor.number object

Usage

## S3 method for class 'factor.number'
plot(x, ...)

Arguments

Value

NULL, printed to console

See Also

factor.number

Examples

library(fnets)
## Alessi, Barigozzi, and Capasso method for restricted models
abc <- factor.number(data.restricted, fm.restricted = TRUE)
print(abc)
plot(abc)

## Eigenvalue ratio method
er <- factor.number(data.restricted, method = "er", fm.restricted = TRUE)
print(er)
plot(er)

## Hallin and Liška method for unrestricted models
hl <- factor.number(data.unrestricted, fm.restricted = FALSE)
print(hl)
plot(hl)

Plotting the networks estimated by fnets

Description

Plotting method for S3 objects of class fnets. When display = "network" or display = "heatmap", it produces a plot visualising three networks underlying factor-adjusted VAR processes: (i) directed network representing Granger causal linkages, as given by estimated VAR transition matrices summed across the lags, (ii) undirected network representing contemporaneous linkages after accounting for lead-lag dependence, as given by partial correlations of VAR innovations, (iii) undirected network summarising (i) and (ii) as given by long-run partial correlations of VAR processes. Edge widths are determined by edge weights. When display = "tuning", it produces up to two plots (when do.larpc = TRUE) visualising the outcome of CV or IC adopted for selecting the l1-regularisation parameters and the VAR order.

Usage

## S3 method for class 'fnets'
plot(
  x,
  type = c("granger", "pc", "lrpc"),
  display = c("network", "heatmap", "tuning"),
  names = NA,
  groups = NA,
  group.colours = NA,
  ...
)

Arguments

Value

A plot produced as per the input arguments

See Also

fnets

Examples

out <- fnets(data.unrestricted,
  do.threshold = TRUE,
  var.args = list(n.cores = 2)
)
plot(out, type = "granger", display = "network",
groups = rep(c(1,2), 50/2), group.colours = c("orange","blue"))
plot(out, type = "lrpc", display = "heatmap")
plot(out, display = "tuning")

Plotting the thresholding procedure

Description

Plotting method for S3 objects of class threshold. Produces a plot visualising three diagnostics for the thresholding procedure, with threshold values t_k (x axis) against (i) Ratio_k, the ratio of the number of non-zero to zero entries in the matrix, as the threshold varies (ii) Diff_k, the first difference of Ratio_k (iii) |CUSUM_k|, the absolute scaled cumulative sums of Diff_k

Usage

## S3 method for class 'threshold'
plot(x, plots = c(TRUE, FALSE, TRUE), ...)

Arguments

Details

See Owens, Cho and Barigozzi (2024+) for further details.

Value

A network plot produced as per the input arguments

References

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

threshold

Examples

## Not run: 
library(fnets)
out <- fnets(data.unrestricted,
   var.args = list(n.cores = 2)
)
# Granger-causal network
th1 <- threshold(out$idio.var$beta)
plot(th1)
print(th1)
# Partial correlations
th2 <- threshold(out$lrpc$pc)
# Long-run partial correlations
th3 <- threshold(out$lrpc$lrpc)

## End(Not run)

internal function for plot.fnets and network

Description

internal function for plot.fnets and network

Usage

plot_internal(
  x,
  type = c("granger", "pc", "lrpc"),
  display = c("network", "heatmap"),
  names = NA,
  groups = NA,
  group.colours = NA,
  ...
)

Forecasting for factor models

Description

Produces forecasts of the data input to object for a given forecasting horizon by estimating the best linear predictors of the common component

Usage

## S3 method for class 'fm'
predict(object, n.ahead = 1, fc.restricted = TRUE, r = c("ic", "er"), ...)

Arguments

Value

a list containing

See Also

fnets.factor.model

Examples

out <- fnets.factor.model(data.restricted, fm.restricted = TRUE)
pre <- predict(out)

Forecasting by fnets

Description

Produces forecasts of the data for a given forecasting horizon by separately estimating the best linear predictors of common and idiosyncratic components

Usage

## S3 method for class 'fnets'
predict(
  object,
  newdata = NULL,
  n.ahead = 1,
  fc.restricted = TRUE,
  r = c("ic", "er"),
  ...
)

Arguments

Value

a list containing

See Also

fnets

Examples

out <- fnets(data.restricted, q = 2, do.lrpc = FALSE, var.args = list(n.cores = 2))
pre.unr <- predict(out, fc.restricted = FALSE)
pre.res <- predict(out, fc.restricted = TRUE)

Print factor number

Description

Prints a summary of a factor.number object

Usage

## S3 method for class 'factor.number'
print(x, ...)

Arguments

Value

NULL, printed to console

See Also

factor.number

Examples

library(fnets)
## Alessi, Barigozzi, and Capasso method for restricted models
abc <- factor.number(data.restricted, fm.restricted = TRUE)
print(abc)
plot(abc)

## Eigenvalue ratio method
er <- factor.number(data.restricted, method = "er", fm.restricted = TRUE)
print(er)
plot(er)

## Hallin and Liška method for unrestricted models
hl <- factor.number(data.unrestricted, fm.restricted = FALSE)
print(hl)
plot(hl)

Print factor model

Description

Prints a summary of a fm object

Usage

## S3 method for class 'fm'
print(x, ...)

Arguments

Value

NULL, printed to console

See Also

fnets.factor.model

Examples

out <- fnets.factor.model(data.restricted, q = "ic")
print(out)

Print fnets

Description

Prints a summary of a fnets object

Usage

## S3 method for class 'fnets'
print(x, ...)

Arguments

Value

NULL, printed to console

See Also

fnets

Examples

out <- fnets(data.restricted, q = 2,
do.lrpc = FALSE, var.args = list(n.cores = 2))
print(out)
x <- sim.var(500, 50)$data
out <- fnets.var(x,
n.cores = 2)
print(out)

Print threshold

Description

Prints a summary of a threshold object

Usage

## S3 method for class 'threshold'
print(x, ...)

Arguments

Value

NULL, printed to console

See Also

threshold

Examples

## Not run: 
library(fnets)
out <- fnets(data.unrestricted,
   var.args = list(n.cores = 2)
)
# Granger-causal network
th1 <- threshold(out$idio.var$beta)
plot(th1)
print(th1)
# Partial correlations
th2 <- threshold(out$lrpc$pc)
# Long-run partial correlations
th3 <- threshold(out$lrpc$lrpc)

## End(Not run)

Simulate data from a restricted factor model

Description

Simulate the common component following an unrestricted factor model that admits a restricted representation; see the model (C2) in the reference.

Usage

sim.restricted(n, p, q = 2, heavy = FALSE)

Arguments

Value

a list containing

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

common <- sim.restricted(500, 50)

Simulate data from an unrestricted factor model

Description

Simulate the common component following an unrestricted factor model that does not admit a restricted representation; see the model (C1) in Barigozzi, Cho and Owens (2024+)

Usage

sim.unrestricted(n, p, q = 2, heavy = FALSE)

Arguments

Value

a list containing

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

common <- sim.unrestricted(500, 50)

Simulate a VAR(1) process

Description

Simulate a VAR(1) process; see the reference for the generation of the transition matrix.

Usage

sim.var(n, p, Gamma = diag(1, p), heavy = FALSE)

Arguments

Value

a list containing

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

idio <- sim.var(500, 50)

Static PCA

Description

Static PCA

Usage

static.pca(
  xx,
  q = NULL,
  q.method = c("ic", "er"),
  q.max = NULL,
  ic.op = 2,
  mm = NULL
)

Threshold the entries of the input matrix at a data-driven level

Description

Threshold the entries of the input matrix at a data-driven level. This can be used to perform edge selection for VAR parameter, inverse innovation covariance, and long-run partial correlation networks.

Usage

threshold(mat, path.length = 500)

Arguments

Details

See Owens, Cho & Barigozzi (2024+) for more information on the threshold selection process

Value

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

References

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

plot.threshold, print.threshold

Examples

## Not run: 
library(fnets)
out <- fnets(data.unrestricted,
   var.args = list(n.cores = 2)
)
# Granger-causal network
th1 <- threshold(out$idio.var$beta)
plot(th1)
print(th1)
# Partial correlations
th2 <- threshold(out$lrpc$pc)
# Long-run partial correlations
th3 <- threshold(out$lrpc$lrpc)

## End(Not run)

Plotting output for tuning parameter selection in fnets

Description

Tuning plots for S3 objects of class fnets. Produces up to two plots visualising CV and IC procedures for selecting tuning parameters and the VAR order.

Usage

tuning_plot(x, ...)

Arguments

Details

See Owens, Cho and Barigozzi (2024+) for further details.

Value

CV/IC plot for the VAR component, and CV plot for the lrpc component (when x$do.lrpc = TRUE).

References

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

fnets

Dantzig selector-type estimator of VAR processes via constrained l1-minimisation

Description

Dantzig selector-type estimator of VAR processes via constrained l1-minimisation

Usage

var.dantzig(GG, gg, lambda, symmetric = "min", n.cores = 1)

Lasso-type estimator of VAR processes via l1-regularised M-estimation

Description

Lasso-type estimator of VAR processes via l1-regularised M-estimation

Usage

var.lasso(GG, gg, lambda, symmetric = "min", n.iter = 100, tol = 0)

Cross validation for factor-adjusted VAR estimation

Description

Cross validation for factor-adjusted VAR estimation

Usage

yw.cv(
  xx,
  method = c("lasso", "ds"),
  lambda.max = NULL,
  var.order = 1,
  n.folds = 1,
  path.length = 10,
  q = 0,
  kern.bw = NULL,
  n.cores = 1
)

Information criterion for factor-adjusted VAR estimation

Description

Information criterion for factor-adjusted VAR estimation

Usage

yw.ic(
  xx,
  method = c("lasso", "ds"),
  lambda.max = NULL,
  var.order = 1,
  penalty = NULL,
  path.length = 10,
  q = 0,
  kern.bw = NULL
)