Several people have asked me for more details about testing for Granger (non-) causality in the context of non-stationary data. This was prompted by my brief description of some testing that I did in my "C to Shining C" posting of 21 March this year. I have an of example to go through here that will illustrate the steps that I usually take when testing for causality, and I'll use them to explain some of pitfalls to avoid. If you're an EViews user, then I can also show you a little trick to help you go about things in an appropriate way with minimal effort.
In my earlier posting, I mentioned that I had followed the Toda and Yamamoto (1995) procedure to test for Granger causality. If you check out this reference, you'll find you really only need to read the excellent abstract to get the message for practitioners. In that sense, it's rare paper!
It's important to note that there are other approaches that can be taken to make sure that your causality testing is done properly when the time-series you're using are non-stationary (& possibly cointegrated). For instance, see Lütkepohl
(2007, Ch. 7).
It's important to note that there are other approaches that can be taken to make sure that your causality testing is done properly when the time-series you're using are non-stationary (& possibly cointegrated). For instance, see Lütkepohl
(2007, Ch. 7).
The first thing that has to be emphasised is the following: