I am interested in forecasting with a vector error correction model (VECM). I am facing a problem of not being able to transform a cointegrated series into a VECM model using the stationary series.
In multivariate forecast like VAR or VECM it is important to see which of the two models to use for forecasting. To decide whether to use a VAR or a VECM:
- First, we do a cointegration test using the
ca.jofunction from "urca" package in R.
- If we find that there is no cointegrating vector suggested by the Johansen procedure, then we can run a VAR model. But if we find evidence of cointegration then we have to use a VECM model in order to incorporate the error correction coefficients in the model.
- To test if there is cointegration in the series we use Johansen test on the data in levels, i.e. in non-stationary form. But after we find evidence of cointegration we have to incorporate as many cointegrating vectors in the VECM as the number suggested by the Johansen test. But then this time the VECM should have been run on stationary series having made them differenced.
- But in R I am not getting the option as to how to make a VECM model differenced and then forecast it. R manuals are suggesting that we should use the function
vec2varto convert a VECM to a VAR model and then forecast the VAR model thus obtained.
- But the VAR model thus obtained from the VECM is at levels and not at differenced form. Hence, inference from this may be biased.
I just want to run a VECM in differenced series (not in levels) and also to include the error correction term. Please help me with this.