[Limdep Nlogit List] MNL Dummy Variable issues

[contactemt]

Hi Tom/All.

So how does one handle the  "category variables" I described?
For instance, to copy from a simple example, say I ask many people to pick 
the best local shop in their area.
They live in different areas so the choice set size as well as the choices 
differ - just the attributes for each shop are the same.

Fine -  NLOGIT works as expected.

I then hypothesise that the sex of the respondent may impact on their 
choice. How can I integrate that into the model?
The GENDER dummy variable will be constant across the one choice set but 
differ across sets.

Any ideas?

Thanks

> You cannot have a constant attribute or variable across all alternatives 
> in a
> choice set.  That is causing your error.
>
> Tom Eagle
>
> -----Original Message-----
> From: limdep-bounces at limdep.itls.usyd.edu.au
> [mailto:limdep-bounces at limdep.itls.usyd.edu.au] On Behalf Of contactemt
> Sent: Thursday, October 12, 2006 9:36 AM
> To: limdep at limdep.itls.usyd.edu.au
> Subject: [Limdep Nlogit List] MNL Dummy Variable issues
>
> Hi,
>
> I have a discrete choice model with no universal choice set and a variable
> number of choices.
>
> I code it thus:
>
> NLOGIT ; Lhs = CHOICE, SETSIZE
>    ; Rhs = v
>    ; Prob = probs $
>
> Where v is the set of attributes common across all choices ( may be 
> several
> dozen).
>
> This seems to work fine, with each attribute returned as a variable 
> parameter.
>
> Now I add a further "category type" variable coded as a dummy (1,0) or 
> (1,-1)
> The variable is constant across an individual set  of alternatives (it 
> describes
> a category)
> but does vary across choices as one would expect.
>
> I thought the above model could handle this type of attribute without
> adjustment.
>
> However, when running the enhanced model I get:
>
> "
> Hessian is not positive definite at start values.
>  Error   803: Hessian is not positive definite at start values.
> B0 is too far from solution for Newton method.
> Switching to BFGS as a better solution method.
> "
>
> And several of the variables are now returned as fixed parameters.
> Also, if I change the value of just one element in a choice set from 1 
> to -1 (or
> vice versa) the model runs
> as before, so clearly the fact that the dummy variable does not change 
> within a
> choice set causes the
> issue.
>
> Is this to be expected and what substantive effects does this have on my 
> model?
>
> I may have, in the full dataset, several dummy variables which describe
> categories for the choice set and
> be constant across a choice set as above. I wanted to produce a "global 
> model"
> and combine these
> categories into one set of data. By doing so, using a larger dataset, I 
> hoped to
> increase the accuracy of
> the parameter esitimates and so the model when classifying new data.
>
> So my questions are:
>
> Can I just plough ahead and accept that some/many of the variables will be 
> fixed
> parameters in my general
> model or should I run each category as a separate model? - the danger here 
> would
> be the datasets, given
> the number of attributes may not be large enough.
> Is there a transformation I could/should apply to the category(s) which 
> will
> allow the "global model" to
> run "better".
>
> Thanks
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