HDGC_HVAR: Test Granger causality in High Dimensional HVARs

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

Test Granger causality in High Dimensional HVARs

Description

Test Granger causality in High Dimensional (Stationary) Heterogeneous VARs

Usage

HDGC_HVAR(
  GCpair,
  data,
  log = TRUE,
  bound = 0.5 * nrow(data),
  parallel = FALSE,
  n_cores = NULL
)

Arguments

GCpair	a named list with names GCto and GCfrom containing vectors of the relevant GC variables.
data	the data matrix or an object that can be transformed to a matrix containing (stationary) realized volatilities.
log	default is TRUE, if the realized volatilities are already log transformed then put =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

LM Chi-square test statistics (asymptotic), LM F-stat with finite sample correction, LM Chi-square (asymptotic) with heteroscedasticity correction, all with their corresponding p-value. Lasso selections are also 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

HDGC_HVAR(GCpair=list("GCto"="Var 1", "GCfrom"="Var 5"), data=sample_RV,log=TRUE)

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