This is exactly the information I was looking for. Thank you!
---- Original message ----
>Date: Sat, 02 Nov 2002 23:01:47 +0900
>From: Joseph Coveney <[email protected]>
>Subject: Re: st: xtgee vs xtlogit
>To: [email protected]
>
>Marie Olson posted a general question about the use of
-xtgee- and
>-xtlogit-.
>
>---------begin excerpted post---------
>
>Can anyone tell me why I might choose xtgee over xtlogit when
>analyzing a dataset of 30 countries for a period of 41
years? My
>variables are the following:
>dep v: war/no war
>indeps: policy/no policy, regime score, gdp change, peace years
>The hypothesis is that certain types of policies are
associated with
>the occurrence of violence, controlling for regime score, gdp
>fluctuations, and the number of peace years prior to the
current
>year.
>
>---------end excerpted post---------
>
>Gary King (http://gking.harvard.edu/stats.shtml) has looked into
>statistical models for relatively rare events in similar
kinds of
>surveys, and has made ReLogit available for Stata.
>
>As to Marie's question, I'm no expert, but -xtgee- (or
-xtlogit, pa-)
>would seem to have certain advantages, such as the ability
to use
>autoregressive working correlation structures. With 41
years of
>data, such correlations might be apparent. Parameter estimates
>(regression coefficients) and parameter standard errors from
>population-average generalized estimating equation (PA-GEE)
>approach, as implemented in -xtgee, robust-, are relatively
resilient
>to misspecification of the working correlation structure.
>
>On the other hand, PA-GEE works best with lots of panels; with
>only 30 nations, the panel number might not be sufficient to
give a
>lot of confidence in hypothesis testing results from -xtgee-.
In
>addition, I was led to beleive that PA-GEE isn't so great
with long
>panels--somewhere I had read that panel lengths of around six or
>fewer are ideal. A good source for advice is the user's manual.
>
>If she believes that there is important autocorrelation, it
might be
>worth considering grouping the 41-year span into epochs of
similar
>lengths of time and use -xtgls- on the proportion (or arcsin-
>transformed proportion) of time at-war in each successive
epoch.
>With blocking the time span into epochs, -xtgee- and
-xtlogit- would
>have fewer intervals to cope with, and might be better behaved.
>For these epochs, alternative approaches could be considered,
>such as Poisson regression (or zero-inflated Poisson regression,
>hopefully) for the number of wars or years at war in successive
>epochs.
>
>I have a concern about using number of years at peace as a
>predictor in Marie's statistical model of her data. It would
seem that
>such a predictor and the response variable would be confounded --
>years at peace is implied in a war/no-war dependent variable
in a
>longitudinal survey. Last month, Wiji Arulampalam posted to the
>list about having difficulty getting proper convergence with -
>xtprobit- and she wondered whether it might have to do with the
>presence of a time-lagged variable in the list of predictors
in her
>case. It seems that an analogous situation arises in Marie's
case.
>Perhaps the use of an autocorrelation structure will obviate the
>need for years at peace as a predictor.
>
>Joseph Coveney
>
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