the variables are integrated of order one. Also, a cointegration relationship exists.
in theory, Granger causality test can be performed by i) by checking statistical
significance of the lagged differences of the variables for each vector; this is a
the measure of short-run causality; and ii) by examining the statistical significance of the
the error-correction term for the vector that there exists a long-run relationship.
How can we apply that using Eviews? (an example is attached)
Import file, select variables, right click, open as VAR, go to basics, select VECM.
A way around the Granger-causality testing in a VECM (that has short-run and long-run components) is to use the Toda and Yamamoto (1995) surplus lag Granger-causality test. See Dave Giles blog for a discussion of this - the link is given below.
https://davegiles.blogspot.com/2011/04/testing-for-granger-causality.html?showComment=1320248137553#c4210543007105661619
This method means you do not need to consider the notions of long-run and short-run Granger-causality. This is useful if you simply wish to know whether Y Granger-causes Y for example.
In a VECM long-run Granger-causality is tested using the t-ratio on the error-correction term in each equation. Short-run Granger-causality uses Wald tests to set the coefficients on all lagged differences of on variable to zero in an equation. In EViews I think this needs to be implemented using the make model/system proc.
The above 2 answers are very good so my suggestion is that you implement both methods. That wi help your paper
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