granger_causality: Granger causality test (multivariate).

In bruceR: Broadly Useful Convenient and Efficient R Functions

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] Defaults to 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. Defaults to both: c("F", "Chisq").
file	File name of MS Word (.doc).
check.dropped	Check dropped variables. Defaults to FALSE.

Details

Granger causality test (based on VAR model) examines whether the lagged values of a predictor (or predictors) help to predict an outcome when controlling for the lagged values of the outcome itself.

Granger causality does not necessarily constitute a true causal effect.

Value

A data frame of results.

See Also

ccf_plot, granger_test

Examples

# R package "vars" should be installed
library(vars)
data(Canada)
VARselect(Canada)
vm = VAR(Canada, p=3)
model_summary(vm)
granger_causality(vm)

bruceR documentation built on June 22, 2024, 12:26 p.m.