Re: [Gretl-users] A Newbie Needs Help
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