Can you perform the Granger causality test with VECM?
Since I have I (1) and cointegrated variables, VECM is assumed to implement the Granger causality test. However I didn’t find any function in R, that could perform the Granger Granger causality test for VECM. I would like to ask You, whether someone does know such a function. Here is my example:
How to test for the absence of Granger causality?
“Xis said to Granger-cause Yif Ycan be better predicted using the histories of both Xand Ythan it can by using the history of Yalone.” We can test for the absence of Granger causality by estimating the following VAR model: Yt= a0+ a1Yt-1+ ….. + apYt-p+ b1Xt-1+ ….. + bpXt-p+ ut (1)
How to select number of lags in VECM model?
1 Even for VECM models (e.g., our variables are I (1) and cointegrated), we select number of lags based on information criteria on VAR model on levels of our time series. Functions of the same duty with differing capacity: is to be run on variables in levels (not differenced). Argument K in ca.jo controls the number of lags of VECM model.
When do you go to advanced G-causality?
If you have a “>2”-variable VAR system, you must go to advanced G-causality: Conditional G-causality, Partial G-causality, Harmonic G-causality, Canonical G-causality, Global G-causality etc.
Which is a wrapper for the VECM function?
Impulse response function ( irf) and forecast error variance decomposition ( fevd) This function is just a wrapper for the lineVar, with model=”VECM”. More comprehensive functions for VECM are in package vars.
How is error correction included in the VECM?
The error correction term may be included in each equation of the VECM only once. It is either lagged by 1 or by p where p is the lag order of the VECM; the corresponding representations of the VECM are known as long-run and transitory; it is still the same model, just different representations; we pick the one we like.