I'm trying to make a simple paired Granger procedure using eViews, but first of all I must make the data stationary.
I am using two methods to make sure it is stationary. 1st I check correlograms and then I run a Unit root test. I have made my Y data stationary so far by using second differences (both correlograms and unit root test are ok), but I have problems doing the same with X data. When I check unit root test, i can reject the null hypothesis that my data has a unit root but when I look at correlograms, they aren't within the limitation brackets and shows that data is not stationary. So my question is what should I count on - unit root test or correlograms? Or maybe there is some other way to ensure stationarity of the data? For better understanding, I will add the pics of what I get.
Here is the graph of the data i have -> this is the raw data.
Here is the graph of logged and 2 times differenced data:
And here is the correlograms of the logged and two times differenced data:
Unit root test of the same data:
