st: nested logit and discrete choice data

[Nils Wlömert <nils.wloemert@gmx.net>]

Dear listers,

I am trying to define a nested logit model for discrete choice data  
derived from a conjoint choice experiment, where participants chose  
between 3 different product alternatives plus a base alternative "none  
of these products" in 8 consecutive choice decisions. I would like to  
nest the base "no-choice" option separate from the "real" alternatives  
very much like Haaijer, Kamakura and Wedel (2001) in their paper: http://faculty.fuqua.duke.edu/~kamakura/My%20Reprints/The%20no-choice%20alternative%20in%20conjoint%20choice%20experiments.pdf

That is, the first level of the decision tree is the choice between  
one of the real alternatives (1,2,3) vs. the no choice option (4)  
(captured in the dissimilarity IV parameter) and the second (bottom)  
level is the standard MNL choice probability depending on the product  
attributes within the "real choice" nest. Products are described by 6  
attributes with 4-5 levels each. Hence, there are 6 columns in the  
data set indicating the levels of each attribute (1-5) and a 1,0 dummy  
for the no-choice option (every 4th row). The dependent variable  
"decision" is also a 1,0 dummy. Further, there are columns for  
alternativeID (32 alternatives per respondent), choice-setID (8 sets  
per respondent), taskID (1,2,3 for the real alternatives and 4 for the  
no-choice option) and caseID. I generated a new variable from taskID  
using nlogitgen including  1 "choice" (1,2,3) and 2 "nochoice" (4).

I searched the documentation and web resources (e.g., the paper on  
nested logit by Florian Heiss http://www.stata-journal.com/sjpdf.html?articlenum=st0017) 
. However, I find that this is different from the given examples in  
that the independent variables are not the levels of one specific  
variable (e.g.: restaurant type, travel mode), but rather the levels  
of several different variables (being the 4-5 levels of the 6  
attributes in 6 different rows in the data set). I would be interested  
to learn, how to define a model that accomodates this kind of data  
structure? What would be my variables at the two levels of the  
hierarchy?

Any help is much appreciated!

Thanks & best,

Nils.

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