Description Usage Arguments Details Value Note Author(s) References See Also Examples
Bivariate Granger causality testing for multiple time series.
1 | granger.test(y, p)
|
y |
T x m time series or matrix. |
p |
Lag length to be used for computing the test |
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.
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
.
These are bivariate tests – not block exogeneity tests for a
fitted VAR model. Note also that these tests are highly sensitive to
lag length (p) and the presence of unit roots. Results in the matrix
include row labels for nice printing with xtable()
Patrick T. Brandt
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.
reduced.form.var
for frequentist VAR estimation,
szbvar
for Bayesian VAR estimation with Sims-Zha prior,
var.lag.specification
for VAR lag length testing.
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