For my thesis. It's about how different classification schemes (IMF, de
facto, natural algorithm by Reinhart/Rogoff (2004) affect empirical
findings on the economic performance of exchange rate regimes. Probit
regressions are used to test the impact of the nominal exchange rate
regime on the probability of banking and currency crises onsets
respectively. I was just curious that there is no FIT measure reported
in my tables so far. The few emprical studies on the issue mostly
feature Pseudo-R^2. I reported R^2 for regressios where I uesd OLS and
was wondering what the equivalent for probit regressions would be. As
said before, my search turned out to be rather unseccessful until I
described to Stata-list. So, do you think it might be OK to report no
FIT measure at all?
uwe
Stas Kolenikov wrote:
well the only measure I can think of as really a FIT measure is how
many observations were classified appropriately to their 0/1
categories. With random effects, you don't even have a predicted
probability -- at least marginally, unless you are willing to
integrate out the random effects.
Within GLM framework, there are some reasonable goodness of fit
measures based on deviance and residuals of different kind. Again,
that has an i.i.d. consideration in mind.
What are you going to use this measure for?
On Thu, 07 Oct 2004 19:09:22 +0200, Uwe Berberich <[email protected]> wrote:
Stas,
thanks for your reply. Do you have any suggestions for better goodness
of fit measures in probit regressions on panel data? McFadden R2 or
Pseudo R2 were the ones suggested in various sources and empirical
studies but I'm open for whatever turns out to be useful and meaningful.
I'm just puzzled that *xtprobit* does not return any measure at all. Is
there any theoretical motivation why I could report probit results
without any goodness of fit measure?
Thank you very much
uwe
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