I have a question related to Granger Causality testing.
Is it okay to use a lag-length of lag=1 in my Granger-test? The optimum lag length selection in my R VARselect(data,lag=maxlag,type=trend) model says that lag=1 shows the best and most stable information criteria values according to AIC, BIC and FPE.
I have a 30-year set of quarterly data and I'm using a maxlag of 4.
Best Answer
If your data is stationary, then yes, based on the information you provided it seems OK to use $\text{lag}=1$.
If your data is nonstationary, you should follow Toda-Yamamoto procedure described very explicitly and clearly in Dave Giles' blog post. There are certain important points to pay attention to with respect to lag order selection under cointegrated data (see especially basic steps 5. and 8. of the Toda-Yamamoto procedure in the above source).