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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.

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  • $\begingroup$ Either a VAR in first differences or a vector error correction model (VECM) depending on whether your series are cointegrated. See e.g. this which is a more general case, but easy to simplify. See also this for a general cookbook approach to VAR modelling. $\endgroup$ Commented Feb 14, 2017 at 15:18
  • $\begingroup$ Thanks a lot for the information provided. The only way to identify stationarity of var is to check the stationarity of constituent variables? $\endgroup$ Commented Feb 15, 2017 at 4:48
  • $\begingroup$ Yes, that's right. $\endgroup$ Commented Feb 16, 2017 at 17:54
  • $\begingroup$ @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: stats.stackexchange.com/questions/191851/… $\endgroup$
    – Jacob H
    Commented Jan 7, 2019 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

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  • $\begingroup$ Thank you for the information provide. So if we need to predict from the VECM mode, we need to go for Rolling forecasts? $\endgroup$ Commented Feb 15, 2017 at 16:11

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