I am working with a multivariate time series and using VAR (Vector Autoregression) model for forecasting. My question is What does stationarity actually means in a multivariate framework.
1) I know that if in VAR setup if determinant of inverse of |I-A|matrix has eigen values less than 1 in modulus , the overall VAR system is stable/stationary, but does that mean I can proceed without bothering about differencing the non stationary component present in the multivariate time series
2) How to proceed if one of the component series is non stationary rest are stationary?
3) How to proceed if more than one component time series are non stationary but are " Not Co-integrated"?
Above all are there any other methods to deal with multivariate time series.I am also exploring the machine learning methods