I have a question to which I can't find an answer although I spent really awfully lot of time searching.
I have time series data for about 20 regions of a country. Each time series covers 20 years.
I want to measure to what extent the evolution of the variable x followed one pattern in the regions. I'm sure it did to some extent but I want to argue in my paper that they follow a similar pattern, so I need an exact measure.
I was thinking about just correlating the data for the regions, but the cross-table I receive is not enough for me to conclude how high the similarities were. Any ideas?
I saw the option autocorrelation and crosscorrelation but I guess that's not the thing I'm looking for. It seems that the forecasting option is also not the best way to do that. I want to mention the statistical analysis only at the very beginning of my paper - then I go into qualitative case studies to prove my point, so I don't need to propose a huge model for the the statistical data.
I will appreciate your help.
- EDIT -
My hypothesis is theory-driven. I assume x and y are causaly linked. Theoretically it makes sense. I've investigated the country qualitatively (historical analysis) and it seems to make sense. I've also checked it with country-level statistical data and there's a huge correlation between x and y, but I only have data for 22 years so I'm afraid the correlation is just a coincidence. I'm asking myself for example whether the correlation is not caused by changes in some, few, regions. That would invalidate by causal explanation. That's why I want to see if the data series are correlated - I've seen charts and all the regions seem to follow the pattern visible in the country-level data, but I want to be sure.
I would love to check correlations of x and y on the regional level too! No idea how to do that (but that's less important, simply because I have some doubts whether the regional data I found on y is reliable and have no other data source).