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Re: st: too good to be true : lr test in mlogit?


From   Maarten Buis <maartenlbuis@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: too good to be true : lr test in mlogit?
Date   Mon, 16 May 2011 09:49:26 +0200

On Sat, May 14, 2011 at 11:31 AM, John Litfiba wrote:
> 1) The log likelihood doesnt converge when I try to fit a random or
> fixed effect with xtlogit on my entire dataset..
> I have to chose a very "small" (well, compared to the total size of
> the sample) of about 10000 observations in order to see the results...
> otherwise I get an error message after 3 or 4 iterations

If you do not tell use what the error message is than we obviously
cannot help you. We need to know exactly what you typed and what Stata
told you in return.

> 2) The idea of running lets say M regressions over randomly chose
> samples could be a solution, but it is statistically valid ? I mean if
> I obtain the distribution of the parameters across my M simulation can
> I infer something on the parameters of the simulation that should have
> been done on the entire dataset ?

No, but if you sample correctly a single random sample of higher level
units will be just as valid a sample from your population as your
large sample, just with a smaller N. The added value of additional
observations tends to decrease with sample size, so going from 10 to
11 observations will have a much bigger effect on your inference than
moving from 100 to 101 observations. There are many estimates for
which the difference between 10000 and 10000000 observations is just
negligible (but there are estimates where it will matter, for example
higher order interaction terms or a categorical variables containing a
rarely occurring category).

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany


http://www.maartenbuis.nl
--------------------------
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