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I have been reading and trying out stuff until I am totally confused and back to square one. Could someone please explain the difference between the two methods suggested below?

Suppose I have 10 stock price series that are I(1). I can use Johansen's method to test for co-integration and find appropriate weights for each stock to create a stationary basket from these 10 stocks.

Second approach, I use available VAR (vector auto regression) methods to fit a VAR model on these 10 stocks and find a model that is stable (stationary).

What is the difference between these two approaches? Are they the same because both result in a basket of stocks that is stationary?

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VAR can be applied only if the input series are stationary otherwise VAR may result in spurious correlation. So just evade it. Now the solution to treat the non stationary series to go for cointegrated series (ca.jo test) and if cointegration is viable then build VECM (Vector Error Correction Mechanism). This incorporate the short and long run relationship between series.

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