HDGC_VAR: Test Granger causality in High Dimensional mixed Integrated...

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

Test Granger causality in High Dimensional mixed Integrated and Cointegrated VARs

Description

Test Granger causality in High Dimensional mixed Integrated and Cointegrated VARs

Usage

HDGC_VAR(
  GCpair,
  data,
  p = 1,
  d = 0,
  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	a data matrix or object that can be coerced to a matrix
p	lag length of the 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

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

HDGC_VAR(GCpair=list("GCto"="Var 1", "GCfrom"="Var 2"), data=sample_dataset_I1, p=3, d=2)

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