Granger_Cause_1

GRANGER_CAUSE_1 is a Granger Causality Test that accepts missing data and variable lag models

GRANGER_CAUSE_1 is a Granger Causality Test that accepts missing data, permits fixed or variable lag models and permits Lag 0 for the Y model as well. It also tries to account for multiple comparisons in the F test
when using variable lag models ( models in which the lag chosen is based on the lowest BIC score).
This code is based on Granger_cause by Chandler.
The null hypothesis is that the y does not Granger Cause x.
A user specifies the two series, x and y, along with
the significance level and the maximum number of lags
to be considered.
The function chooses the optimal lag length for x and y based
on the Bayesian Information Criterion ( if using variable lag lengths).
The function produces the F-statistic for the Granger Causality Test
along with the corresponding critical value.
We reject the null hypothesis that y does not Granger Cause x
if the F-statistic is greater than the critical value.
Type help granger_cause_1 to learn more.