MSBVAR (version 0.9-2)

granger.test: Bivariate Granger causality testing

Description

Bivariate Granger causality testing for multiple time series.

Usage

granger.test(y, p)

Arguments

y

T x m time series or matrix.

p

Lag length to be used for computing the test

Value

A matrix with 2 columns. Column 1 are the F-statistic values. Column 2 are the p-values for the F-tests. Row labels specifying the Granger causality relationship tested will be included if variables in the input time series y include variable or dimnames.

Details

Estimates all possible bivariate Granger causality tests for m variables. Bivariate Granger causality tests for two variables X and Y evaluate whether the past values of X are useful for predicting Y once Y's history has been modeled. The null hypothesis is that the past p values of X do not help in predicting the value of Y.

The test is implemented by regressing Y on p past values of Y and p past values of X. An F-test is then used to determine whether the coefficients of the past values of X are jointly zero.

This produces a matrix with m*(m-1) rows that are all of the possible bivariate Granger causal relations. The results include F-statistics and p-values for each test. Tests are estimated using single equation OLS models.

References

Granger, C.W.J. 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods" Econometrica 37:424-438. Sims, C.A. 1972. "Money, Income, and Causality" American Economic Review. 62:540-552.

See Also

reduced.form.var for frequentist VAR estimation, szbvar for Bayesian VAR estimation with Sims-Zha prior, var.lag.specification for VAR lag length testing.

Examples

data(IsraelPalestineConflict)
granger.test(IsraelPalestineConflict, p=6)