is Toda-yamamoto test used for long or short term ? and what is the difference between long and short term? short term is 5 years? more less? can we apply Toda-yamamoto to 10 years data or not? if not is there any alternative test?
Thank you
The Toda Yamamoto test for Granger Causality is used for non-stationary variables. It is similar to the standard Granger test for Granger Causality in a VAR with stationary variables. It does not distinguish between long and short-run effects.
In a VECM we have equations like
∆Xt = (error correction terms)t-1 + first difference terms
Granger Causality may arise in two ways -
If 1. some economists refer to the effect as long run causality because it rises from the return to long-run equilibrium caused by the ecm terms. The actual time to return to equilibrium is determined by the coefficients on the ecm terms. 2. is a persistence effect and some economists refer to this as a short-run effect. Time taken maydepend on the number of lags in the system and the value of the coefficients. It has nothing to do with 5 or 10 years.
Direct tests on 1 and 2 are difficult to implement.
You say that you have 10 years of data. 10 years of quarterly data is probably not enough. You will probably not reject non-causality in any of your tests. !0 years of monthly data may work but it depends on the nature of your data set and your underlying economic theory. If the adjustment process is very slow (exchange rates) 10 years will not be sufficient. It also depends on the complexity of your model
thank you very much for your answer
do ypou have any recommendation (books, article) that discuss this matter
Hi Fatima,
please take a look at the following link: https://davegiles.blogspot.com/2011/04/testing-for-granger-causality.html
I think that it will be very helpful (especially if you use the Eviews software). It explains the procedure step by step.
The test does not distinguish between long and short-run effects.
The Toda-Yamamoto approach is a powerful tool for understanding the dynamics of economic variables over time. It can reveal both immediate (short-run) and persistent (long-run) effects, providing valuable insights for policy-making and economic forecasting.