HDGC_VAR_multiple_pairs_I0: Test multiple pairs Granger causality in High Dimensional...

In Marga8/HDGCvar: Granger Causality Testing in High Dimensional Vector Autoregressive Models

Test multiple pairs Granger causality in High Dimensional Stationary VARs

Description

A wrapper around HDGC_VAR_multiple_I0. If GCpairs is used, the function is the same as HDGC_VAR_multiple_I0. Alternatively, if we want to test all combinations between variables in GCto and GCfrom, these can be given directly. See Example.

Usage

HDGC_VAR_multiple_pairs_I0(
  data,
  GCpairs = NULL,
  GCto = NULL,
  GCfrom = NULL,
  p = 1,
  bound = 0.5 * nrow(data),
  parallel = FALSE,
  n_cores = NULL
)

Arguments

data	the data matrix or object that can be coerced to a matrix.
GCpairs	it should contain a nested list. The outer list is all the pairs to be considered. See HDGC_VAR_multiple_I0. The inner list contains the GCto and GCfrom vectors needed for HDGC_VAR_I0.
GCto	all combination variables Granger caused
GCfrom	all combination variables Granger causing
p	lag length of the VAR
bound	lower bound on tuning parameter lambda
parallel	TRUE for parallel computing
n_cores	nr of cores to use in parallel computing, default is all but one

Value

Granger causality matrix and Lasso selections are printed to the console

References

Hecq, A., Margaritella, L., Smeekes, S., "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure." arXiv preprint arXiv:1902.10991 (2019).

Examples

## Not run: GCto = list(c("Var 1", "Var 2")); GCfrom = list(c("Var 3", "Var 4", "Var 5"))
## Not run: HDGC_VAR_multiple_pairs_I0(sample_dataset_I0,GCto,GCfrom,p=2)

Marga8/HDGCvar documentation built on May 25, 2024, 11:12 a.m.