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Are there any suggested approaches for using non-stationary series in a VAR model? As per otexts.org:

If the series are non-stationary we take differences to make them stationary and then we fit a VAR model (known as a “VAR in differences”).

Are there any other approaches for creating a forecasting model non-stationary series in a multivariate series?

Any leads on this would be helpful. I'm looking for implementing this model in R.

Tavrock
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Lal Prasad R
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  • Either a VAR in first differences or a vector error correction model (VECM) depending on whether your series are cointegrated. See e.g. [this](http://stats.stackexchange.com/questions/148994/var-or-vecm-for-a-mix-of-stationary-and-nonstationary-variables/149263#149263) which is a more general case, but easy to simplify. See also [this](http://stats.stackexchange.com/questions/191851/var-forecasting-methodology/195477#195477) for a general cookbook approach to VAR modelling. – Richard Hardy Feb 14 '17 at 15:18
  • Thanks a lot for the information provided. The only way to identify stationarity of var is to check the stationarity of constituent variables? – Lal Prasad R Feb 15 '17 at 04:48
  • Yes, that's right. – Richard Hardy Feb 16 '17 at 17:54
  • @LalPrasadR you DON'T need to first difference data to forecast with VAR (sorry this is one of my pet pervs)!!! Read my post here: https://stats.stackexchange.com/questions/191851/var-forecasting-methodology/195477#195477 – Jacob H Jan 07 '19 at 19:07

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To use VAR model for non-stationary series, you have to test the cointegration If there is cointegration you use the model VECM Otherwise a VAR on the first differences of the variables

Tchagrie
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  • Thank you for the information provide. So if we need to predict from the VECM mode, we need to go for Rolling forecasts? – Lal Prasad R Feb 15 '17 at 16:11