[Gretl-users] A Newbie Needs Help

I took a class in causal modeling more than 10 years ago, and while I thought I remembered the basics, since then all my work has been with structural equation models, and I find I'm now a bit lost.... Let me explain what I'm trying. Basically, I'm trying to test the hypothesis that high levels of press freedom tend to prevent violent ethnic conflict, because ethnic groups can fight things out in the media. Therefore, the main effect I'm looking for is an effect of media freedom and ethnic violence, and my guess is that effect will be a bit lagged, though I'm not sure of that, and it's also possible that each variable affects the other. I have data at least back to 1990 in many, many countries for both of these, though I intend to do the tests just in sub-Saharan Africa and post-Communist Europe. Other endogenous variables that could affect the equation would be democracy (the Freedom House political freedom score), unemployment, and change in per-capita GDP. I'm working on figuring out exogenous variables, but election years and possibly the presence of droughts look good, and literacy rates (separately for men and women) might also be useful. My question is, how do I frame this. Basically, I should have time series data for each variable for each of the countries in question. Each country could therefore be analyzed individually, but I'd ideally expect patterns within particular regions, if not across regions. My memory vaguely recalls that I want to use SURE or some kind of simultaneous equations analysis, but I've been looking through the two relevant texts I have (Gujarati, Third Edition, and Hamilton's Time Series Analysis), and come to the conclusion that I'm a lot less smart than I thought I was, at least on this subject. Could anyone give me a few pointers? And if those pointers included tips on setting this up in GRETL, that would also help. One specific question I have what do to with exogenous variables that don't vary much over time. To wit, I'm suspect literacy rates play a role, but since they don't change much over time, that roles should be seen across countries rather than over time within countries (which is one reason a multiple-country analysis would be useful). Thanks, Scott Orr