type -ereturn list- to find out. The likelihood is stored in e(ll),
and then you can do whatever you need to it.
To my opinion, pseudo-R^2 are lousy measures, and more so in the panel
data. Even in the linear model, you would have the within panel and
the between panel and the total sum of squares and hence R^2. That's
something not so well quantifiable even in the linear regression: it
would involve subtracting one sum of squares from another, and you are
not even guaranteed to have your sum of squares positive -- this was
mentioned on the list recently. For probit regressions, you would have
a hard time convincing me any of the pseudo-R^2 gives the same
messages as does R^2 in linear model.
On Thu, 07 Oct 2004 16:27:04 +0200, Uwe Berberich <uwe@polarmond.de> wrote:
> I apologize for bringing up my likely trivial question yet again.
> However, I still couldn't find a way to report a goodness of fit measure
> for my Probit-regressions. I used random effects *XTPROBIT* on an
> unbalanced panel set. I already checked STATAs FAQ and previous list
> entries. Unfortunately, most entries either pointed towards the
> *"fitstat" *command that doesn't work with XTPROBIT or towards manual
> calculations unsing the constant-only model and the full model. I'm used
> to this procedure in theory, though I don't know where to find the
> corresponding numbers in my STATA output.
--
Stas Kolenikov
http://stas.kolenikov.name
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