One decision that we often have to make when modelling with time-series data is whether to use "seasonally adjusted" data, or the original (unadjusted) data. In some cases the decision is effectively made for us - only the seasonally adjusted data are published. This arises, for example, with some U.S. macroeconomic data, and it can be a bit of a pain.
For some previous comments on this, see here.
However, suppose that we have a choice - original data, or data that have been seasonally adjusted by some filtering method (e.g., the Census X-11/12/13 filter) - and we're interested in testing for Granger causality. Is there any evidence in favour of using one version of the data or the other?
Well, yes, there is. Let's take a look at it.