HDGC_HVAR_all: Granger Causality Network in High Dimensional HVARs

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

Granger Causality Network in High Dimensional HVARs

Description

Wrapper around HDGC_HVAR_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_HVAR_all(
  data,
  log = TRUE,
  bound = 0.5 * nrow(data),
  parallel = FALSE,
  n_cores = NULL
)

Arguments

data	the data matrix or an object that can be coerced to a matrix containing (stationary) realized volatilities
log	default is TRUE, if the realized volatilities are already log transformed then put to FALSE
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

A 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).

Corsi, Fulvio. "A simple approximate long-memory model of realized volatility." Journal of Financial Econometrics 7.2 (2009): 174-196.

Examples

## Not run: HDGC_HVAR_all(data=sample_RV, log=TRUE, parallel = TRUE) 

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