HDGC_VAR_all: Granger Causality Network in High Dimensional mixed...

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

Granger Causality Network in High Dimensional mixed Integrated and Cointegrated VARs

Description

Wrapper around HDGC_VAR_multiple which tests Granger causality from each variable to all other variables, one by one. Can therefore be used to construct a network.

Usage

HDGC_VAR_all(
  data,
  p = 1,
  d = 0,
  bound = 0.5 * nrow(data),
  parallel = FALSE,
  n_cores = NULL
)

Arguments

data	the data matrix or object that can be coerced to a matrix.
p	lag length of VAR
d	order of lag augmentation corresponding to suspected max order of integration
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., "Inference in Non Stationary High Dimensional VARs" (2020, check the latest version at https://sites.google.com/view/luca-margaritella )

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: HDGC_VAR_all(data=sample_dataset_I1, p=2, d=2, parallel=TRUE )

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