[Gretl-users] A Newbie Needs Help

[Scott David Orr]

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