Method
If a time series is stationary, the test is performed using the level values of two (or more) variables. If the variables are non stationary, then the test is done using first (or higher) differences. The number of lags to be included is usually chosen using an information criteria, such as the Akaike information criterion or the Schwarz information criterion. Any particular lagged value of one of the variables is retained in the regression if (1) it is significant according to a t-test, and (2) it and the other lagged values of the variable jointly add explanatory power to the model according to an F-test. Then the null hypothesis of no Granger causality is retained if and only if no lagged values of an explanatory variable have been retained in the regression.
In practice it may be found that neither variable Granger-causes the other, or that each of the two variables Granger-causes the other.
Read more about this topic: Granger Causality
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