Am 10.03.2008 23:57, Scott David Orr schrieb: > 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.... You have my sympathy and understanding, but I doubt that there's any quick solution to your problem... > > 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. The question is if you're ready to assume and then exploit some degree of homogeneity (equal parameter values) across countries. If so, you're in a panel context. If not, then you could use SURE. Country-per-country is also admissible, it's all a matter of efficiency and sample size. The bigger problem that I see is your set of endogenous explanatory variables, so you may have to use some instrumental-variables approach. > > Could anyone give me a few pointers? And if those pointers included > tips on setting this up in GRETL, that would also help. Putting all the ingredients together is definitely doable but is a full-fledged research project I'd say. As I said, I don't think there's a quick solution. 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). Yes then you need a panel analysis. However, those time-constant variables are hard (if not impossible) to distinguish from (other) fixed effects. So you would have to hope you don't need to use a fixed-effects model. cheers, sven _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users