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Re: st: Re: A fractional logit can't converge. Anyway, backed up results are useful for mfx? how?

From   Roger Cremades Rodeja <[email protected]>
To   [email protected]
Subject   Re: st: Re: A fractional logit can't converge. Anyway, backed up results are useful for mfx? how?
Date   Thu, 8 Mar 2012 16:12:08 +0100

Dear Marteen and Nick,

I am extremely grateful for your help and clarity. Using
-technique(bhhh)- it worked perfectly.

Marteen, yes I do have a big spike, you were right! I use proportion
data of 1,300 households, but most of them have a 0 in my dep var, so
I though it was because of that, as in my other two models just change
the dep var and it has more variation.

Nick, I meant logit (I spelt tobit wrongly).

Bless you!!

El día 8 de marzo de 2012 14:29, Maarten Buis <[email protected]> escribió:
> On Thu, Mar 8, 2012 at 1:14 PM, Roger Cremades Rodeja
> <[email protected]> wrote:
>>  I am using fractional tobit "glm depvar indepvar, link(logit)
>>   family(binomial)  vce(robust)". I successfully used it in other two models,
>>  but in this case after some iterations Stata says "backed up" and cannot
>>  converge, then I create a table with the results (outreg), and still appears
>>  a resulting table with results that are consistent with my other two models,
>>  do those results makes statistical sense?
> They make no sense, so don't look at them and certainly don't try to
> publish anything with them.
>>  I want to create then elasticities with "mfx" but I can't because the
>>  model did not converge, if those results produced with outreg (previous
>>  line) make a bit of sense, how can I create elasticities with them using
>>  Stata?
> Don't do that.
>>  Also, after typing the command appear "note: perc_mod_com has noninteger
>>  values", I do not understand why, as I think proportion variables should be
>>  0 to 1.
> That message makes sense: remember that -glm- is a general purpose
> program that can be "tricked" into estimating a fractional logit.
>> Following previous emails in Statalist, I tried delete some
>>  variables and I still couldn't solve it, OLS do not reject any variable, the
>>  model do not work trying to delete indep variables one by one, and I am
>>  trying more and more combinations, but it still does not work.
> I would look for some form of perfect prediction. This becomes more
> likely if you have a very large spike in the dependent variable
> (typically at 0 or 1) or a small sample size.
>>  Please somebody knows if this analysis can be carried on using another
>>  sofware? I need these results..
> I think it is a problem with your data not your software.
> -- Maarten
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
> --------------------------
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