Hello,
Many thanks for the follow-up. I do have a situation where the proportions should add up to one, as in the city budget example.
In my case, my unit of observation is the share of a new car model as a percent of the total market for new cars in a given year. I have this share over several years. Because there are hundreds of car models in my data, I don't think it would be feasible to use a multinomial logit. None of my observation are zero or one. The x's are mostly car attributes for the particular car models, many of which change over time.
I pool all the data and estimate with:
glm share_i_year_t x_i_t, fam(binomial) link(logit) robust
Company dummies are included as fixed effects. The predicted shares from the model sum to 1 for a given year, which is gratifying. Out-of-sample predictions do not sum to one, which is not gratifying but also not unexpected. Maarten's comment makes me wonder if there is a better approach.
Any further insights would be welcome.
-------- Original-Nachricht --------
Datum: Thu, 26 Jul 2007 15:17:43 +0200
Von: "Maarten Buis" <[email protected]>
An: [email protected]
Betreff: st: RE: RE: out-of-sample predictions with GLM
> ---I wrote:
> > A situation where you want proportions to add up to 1
> > within an observation occurs when you have for each
> > observation multiple proportions, e.g. for each city
> > you have the proportions of the city budget spent on
> > safety, education, transport, etc. In this case the
> > proportions within a city should add up to 1. A model
> > that deals with this situation is -dirifit-,see:
> > http://home.fsw.vu.nl/m.buis/software/dirifit.html
>
> A couple of months ago I posted a fractional multinomial
> logit -fmlogit- on the statalist
> http://www.stata.com/statalist/archive/2007-05/msg00449.html
>
> -fmlogit- relates to -dirifit- like fractional logit relates
> to beta regression.
>
> Can you tell us a bit more about your problem? What are
> the dependent and independent variables? Do you have many
> observations with a proportion equal to 0 or 1. Papke and
> Wooldridge are overly optimistic about the ability of
> dealing with those problems using the fractional
> (binomial or multinomial) logit model.
>
> Hope this helps,
> Maarten
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting address:
> Buitenveldertselaan 3 (Metropolitan), room Z434
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
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