Help with cointegration problem

Please excuse me if my question is dumb but I am not a Statistics student. I have a project where I was exploring options for time series analysis where I found some papers on cointegration. I have 4 variables. One of them response variable. I wanted to to the forecasting using the cointegration technique. Please tell me if I am wrong whether the technique to use is a Johansen test. However I understand that the test needs a requirement that the variables are unit root (right?). So I took the software R and did the ADF test in R on each variable (and the first difference ) . But some variables dont pass the adf test. My question is:

• Is my thinking above right?

• Then, what do i do for the variables which dont pass the ADF test for unit root ? Can I still take those in the johansen test? If so, what is the purpose of the ADF test at all?

@mpiktas Here I performed the ADF test and here are the results I got:

• yt.adf Null Hypothesis not rejected (Non Stationary)
• dyt.adf Null Hypothesis not rejected (Non Stationary)
• X1t.adf Null hypothesis rejected (Stationary)
• dX1t.adf Null hypothesis rejected (Stationary)
• X3t.adf Null hypothesis rejected (Stationary)
• dX3t.adf Null Hypothesis not rejected (Non Stationary)
• X4t.adf Null Hypothesis not rejected (Non Stationary)
• dX4t.adf Null Hypothesis not rejected (Non Stationary)

yt.adf is the response variable while dyt.adf is the firs difference. Similarly other variables. As seen here, I suppose the inference is that none (yt, X1t,X2t,X2t,X4t) are unit root? So then I cant apply johansen test for cointegration,can I?

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Can you be more precise about what you mean by "the cointegration technique"? As far as I know, cointegration is when multiple time series, very roughly, are influenced by a common stochastic process. The Johansen test is a way of testing whether there is cointegration. –  Macro Jul 20 '11 at 3:11