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Re: st: SAS vs STATA : why is xtlogit SO slow ?


From   Klaus Pforr <kpforr@googlemail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: SAS vs STATA : why is xtlogit SO slow ?
Date   Fri, 03 Feb 2012 18:14:21 +0100

<>

just some comments on this, although I hope that the person who posted this problem originally will eventually tell us more about the data and the output.

Am 03.02.2012 17:34, schrieb Clyde B Schechter:
I don't really know much about how xtlogit (or any of the other xt estimators) work "under the hood" [that's "under the bonnet" to Nick Cox] but I have used these estimators a fair amount and have some pragmatic tips for dealing with non-convergence of random effects models that have served me well.
I think that he/she wants to estimate a fixed-effects-model (although I'm sure, if this is generally easier or more difficult to estimate than RE)

1. Check all of your categorical predictors.  If any of them have any level that is only instantiated in a small number of cases in the estimation sample, the coefficient for that level can be very difficult to estimate.  Try combining some levels in that variable (or, if it is a dichotomous variable drop it from the model.)

2. Similarly check your continuous variables to be sure the have some reasonable amount of variability in the estimation sample.

3.  Check the scales of your continuous variables to see that they are all in the same "ballpark."  If two variables differ by several orders of magnitude, Stata will often thrash around trying to fit coefficients and ultimately fail.

4. Try providing Stata with starting values of your own using the from() option.  Other responders have already suggested this.  I have a couple of specific suggestions for selecting starting values:

a.  Try the non-xt version of the same model, in this case logit.  See if those values will get Stata over the hump.
b.  Try the population averaged version of the same model.  The population averaged estimator is calcualted using a different approach that seems to be more robust to quirks in the data, and those estimates often work well as starting values for the random effects model.  [Which surprises me, because the population averaged parameters are actually different conceptually and often distant numerically from the corresponding parameters of a random effects model.  But my experience is that they almost always work as a starting point nonetheless.]
Atleast for FE, the implemented estimator uses the pooled coefficients of the logit-model by default. Annother possibility are random starting values, either by turning on the search option with the ml-options, or computing them before and passing them via the from()-option.

Hope this helps.

Clyde Schechter
Department of Family&  Social Medicine
Albert Einstein College of Medicine
Bronx, New York, USA



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best

Klaus

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Klaus Pforr
MZES AB - A
Universität Mannheim
D - 68131 Mannheim
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