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

From   Joerg Luedicke <>
Subject   Re: st: too good to be true : lr test in mlogit?
Date   Fri, 13 May 2011 09:11:00 -0400

On Fri, May 13, 2011 at 6:56 AM, Maarten Buis <> wrote:
> On Fri, May 13, 2011 at 11:21 AM, John Litfiba wrote:
>> But after various tentatives I think that it is not computationally
>> feasible (at least on my PC) to fit a random or fixed effect logit on
>> such a large dataset
> I would not give up on that yet. I would select my model on a sample
> of your data (I would sample higher level units, rather than
> individual observations),

This is what I would do, too. In addition, I would be interested in
the variation of results from different samples. I would recommend
setting up a program that is drawing a random sample, say 1%, running
the model, and saving the results. Then let that program run a 1000 or
whatever times. (How this can be done is described here: Small
variation in results could then be indicative of the fact that it is
pointless anyway to have a sample size of > 2mio. In that case, i.e.
low variation of results, you can also compare your xtlogit model with
the model you have now etc.  Large variation could be an indication of
the fact that a very large sample size may be appropriate. You could
also play with different sample sizes (say, 1%, 3%, 5%) and see how
the variation of results changes across sample size. At the very
least, this will provide useful information about your sample and
might be helpful with respect to the specification of your model.

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