[Limdep Nlogit List] MNL Dummy Variable issues

[contactemt]

Whoops - bad example GENDER is not an attribute of the shop - but I hope you 
get my drift.


>
>
> 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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>> Limdep at limdep.itls.usyd.edu.au
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>>
>>
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>>
>>
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>
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