I am reading Levendis "Time Series Econometrics: Learning Through Replication" (2018) and there are two statements about Granger causality that kinda confuses me. The statements themselves are not confusing, but the implications.
- "Granger causality is much harder to establish with more than 2 variables". So I guess few variables are better, and using just two variables are even more better.
- "Tests of Granger causality are sensitive to omitted variables. Researchers should include all relevant variables in their analysis". So here, more relevant variables are better, I guess.
Can we add more relevant variables without jeopardizing Granger causality?