GrangerTest: Granger Causality Test

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Granger Causality Test

Description

Performs Granger causality test using a vector autoregressive model

Usage

GrangerTest(X,p=1,include.mean=T,locInput=c(1))

Arguments

X	a T-by-p data matrix with T denoting sample size and p the number of variables
p	vector AR order.
include.mean	Indicator for including a constant in the model. Default is TRUE.
locInput	Locators for the input variables in the data matrix. Default is the first column being the input variable. Multiple inputs are allowed.

Details

Perform VAR(p) and constrained VAR(p) estimations to test the Granger causality. It uses likelihood ratio and asymptotic chi-square.

Value

data	Original data matrix
cnst	logical variable to include a constant in the model
order	order of VAR model used
coef	Coefficient estimates
constraints	Implied constraints of Granger causality
aic, bic, hq	values of information criteria
residuals	residual vector
secoef	standard errors of coefficient estimates
Sigma	Residual covariance matrix
Phi	Matrix of VAR coefficients
Ph0	constant vector
omega	Estimates of constrained coefficients
covomega	covariance matrix of constrained parameters
locInput	Locator vector for input variables

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 2)

MTS documentation built on April 11, 2022, 5:07 p.m.