--- [email protected] wrote:
> 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.
The constraint that the proportions add up to 1 in a given year is true
in your data (if you have all models (or a model "other") as is
apperently true in your case). This constraint is however not enforced
in your model. The fact that when you add the predicted proportions up
you get a number close to 1 is primarily the result of your data and is
certainly not a characteristic of your model. With a fractional mlogit
model you could enforce that constaint, though I agree with you that in
your case that is probably unpractical.
A different remark is that you are ignoring the panel structure of your
data. The panel equivalent of -glm- with the -robust- option is
-xtgee-. So you might try:
xtgee share_i_year x_i_t, fam(binomial) link(logit) i(model) t(year)
(assuming that the variable indicating the model is called model, and
the variable indicating the year is called year.)
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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