bruceR (version 0.7.2)

granger_causality: Granger causality test (multivariate).

Description

Granger test of predictive causality (between multivariate time series) based on vector autoregression (VAR) model. Its output resembles the output of the vargranger command in Stata (but here using an F test).

Usage

granger_causality(
  varmodel,
  var.y = NULL,
  var.x = NULL,
  test = c("F", "Chisq"),
  file = NULL,
  check.dropped = FALSE
)

Arguments

varmodel

VAR model fitted using the vars::VAR() function.

var.y, var.x

[optional] Default is NULL (all variables). If specified, then perform tests for specific variables. Values can be a single variable (e.g., "X"), a vector of variables (e.g., c("X1", "X2")), or a string containing regular expression (e.g., "X1|X2").

test

F test and/or Wald \(\chi\)^2 test. Default is both: c("F", "Chisq").

file

File name of MS Word (.doc).

check.dropped

Check dropped variables. Default is FALSE.

Value

A data frame of results.

Details

The Granger causality test (based on VAR model) examines whether the lagged values of a predictor (or predictors) have any incremental role in predicting an outcome if controlling for the lagged values of the outcome itself.

See Also

ccf_plot, granger_test

Examples

# NOT RUN {
# "vars" package should be installed and loaded.
library(vars)
data(Canada)
VARselect(Canada)
vm=VAR(Canada, p=3)
model_summary(vm)
granger_causality(vm)
# }
# NOT RUN {
# }