[Limdep Nlogit List] repost with adapted tables, sorry for inconvenience

Abdulbaki Bilgic tebrik at yahoo.com
Wed Nov 8 10:04:51 EST 2006


Dear Dr. Koetse,
Check E19-43 in LIMDEP V8 for how you can arrange the data in a single
line. I think what you need is that you have to create additional
variables indicating that your temperature and rain variables vary to
choice mode. In such a case, temper1 temper2...temp7 and rain1
rain2...rain7 and the rest is the same. Your model is I guess
conditional logit model mixing of choice mode and individual
characteristics.
Best regards,,,

--- "Koetse, M.J.J. (Mark)" <mkoetse at feweb.vu.nl> wrote:

> 
> L.S.,
> 
> Mr. Feinberg, thanks very much for your early response to our
> question.
> However, our main problem is not that we do not have the information
> on
> unselected choices, but really is on the set up of the data. Below we
> try to describe our problem in more detail.
> 
> Our data set is of the following format 
> 
> Individuals   Choice    Temperature   Rain   	Income   	Distance
> 
> 1		  2		3		  1		5
> 3.5	
> 2		  3		4		  1		4
> 6	
> 3		  4		1		  1		5
> 10	
> 4		  3		4		  2		6
> 5	
> 5		  5		6		  4		3
> 20	
> 6		  6		7		  5		4
> 8	
> 7		  7		9		  1		5
> 25	
> 8		  1		10		  1		1
> 1	
> 9		  4		2		  1		2
> 15	
> 
> Choice, Temperature, rain, income are categorical variables, and
> distance is measured in kilometers. We want to estimate a multinomial
> logit model for this data set format. We assume that "temperature"
> and
> "rain" affect different mode choice to a different extent.
> Furthermore,
> each individual has to make choice among "seven" choices. As far as
> we
> can see, the data set-up in Limdep needs the following format for
> using
> data for discrete models (we outlined individual 1 and 2):
> 
> Individuals	Alternatives Choice Temperature Rain Income Distance	
> 1		1		 0	  3		  1	 5
> 3.5	
> 1		2		 1	  3		  1	 5
> 3.5	
> 1		3		 0	  3		  1	 5
> 3.5	
> 1		4		 0	  3		  1	 5
> 3.5	
> 1		5		 0	  3		  1	 5
> 3.5	
> 1		6		 0	  3		  1	 5
> 3.5	
> 1		7		 0	  3		  1	 5
> 3.5	
> 2		1		 0	  4		  1	 4
> 6	
> 2		2		 0	  4		  1	 4
> 6	
> 2		3		 1	  4		  1	 4
> 6	
> 2		4		 0	  4		  1	 4
> 6	
> 2		5		 0	  4 		  1	 4
> 6	
> 2		6		 0	  4		  1	 4
> 6	
> 2		7		 0	  4		  1	 4
> 6	
> 
> And this setup continues until we have 9*7=63 lines (7 lines per
> individual) for the same data format given above. 
> 
> Now the questions are:
> 
> 1-	Can we Use directly the first data format for estimation of
> multinomial logit? Or it is necessary for using LIMDEP to have our
> data
> in the second format? 
> 2-	If Limdep can not handle the data in the first format. Is there
> any alternative available for it because our data set is very big and
> it
> will take lot of time and efforts to convert it to the second format?
> 3-	And even if we convert data to the second format would it not be
> anything additional but to reproduce the selected choices values
> again
> for all unselected choices?
> 
> Again, any help is appreciated.
> 
> Kind regards,
> 
> Mark Koetse and Muhammad Sabir
> 
> 
> -----Original Message-----
> From: limdep-bounces at limdep.itls.usyd.edu.au
> [mailto:limdep-bounces at limdep.itls.usyd.edu.au] On Behalf Of Fred
> Feinberg
> Sent: 03 November 2006 19:06
> To: Limdep and Nlogit Mailing List
> Subject: Re: [Limdep Nlogit List] (no subject)
> 
> The problem seems more severe than would be addressed by reformatting
> the data.  It's not clear from your description whether you're
> running a
> (McFadden type) conditional logit model -- iin which the
> characteristics
> of the choices themselves would be changing from choice occasion to
> choice occasion -- or a multinomial logit (where you would obtain
> coefficients for characteristics of the decision-maker).  It sounds
> like
> you have elements of both, which is fine.  [Note that programs like
> SPSS
> and SAS, for example, will run multinomial models, but not
> conditional
> ones, and have to be tricked into doing so by, for example, Cox
> regression.]
> 
> Anyway, if you are trying to get some set of coefficients for the
> *attributes* of the choices themselves, as common in transportation
> and
> ubiquitous in marketing (e.g., price changes), then not having those
> attributes for the options that were not chosen is a BIG problem. One
> possibility is to do some type of (perhaps multiple) imputation. 
> This
> is complicated by your need to assume that the he covariates that
> were
> NOT observed are drawn from the same distribution of those that were.
> For example, if we don't observe the price of something you didn't
> buy,
> can we assume it's drawn from the same distribution of the prices
> that
> WERE observed?  That's rarely true.  Usually, you'll need a separate
> model for the unobserved outcomes, which is where Heckman-type models
> can be useful.
> 
> Joe Ibrahim (http://www.hsph.harvard.edu/facres/ibrhm.html) has done
> a
> lot of work on this and related problems, primarily from a Bayesian
> perspective, which handles missing data in a natural fashion.  He may
> have written something on your specific problem.  A good general
> reference is:
> 
> Ibrahim JG, Chen MH, and Lipsitz SR. Missing Responses in Generalized
> Linear Mixed Models When The Missing Data Mechanism is Nonignorable,
> Biometrika, 2001; 88:551-564.
> 
> FF
> 
> 
> "Koetse, M.J.J. (Mark)" wrote:
> 
> > L.S.
> >
> >
> >
> > We are doing some research in which we want to estimate a
> multinomial
> > logit model on transport mode choices by individuals. We only have
> > information on the choices that individuals make, some
> characteristics
> >
> > of the transportation mode chosen, and on individual
> characteristics.
> > We
> > do not have any information about the choices that are not
> selected.
> >
> >
> >
> > As far as we can see, the data set-up in Limdep requires
> information
> > about the selected choice as well as the non-selected choices. This
> > implies that we need to convert our single line observations (in
> which
> >
> > one line represents one individual) to multiple line observations
> (in
> > which one individual is represented by a number of lines that is
> equal
> >
> > to the number of alternatives in the choice set). However, since we
> > have
> > no specific information on these possible non-selected
> alternatives,
> > this would imply that these extra rows contain identical
> information
> > as
> > the original row.
> >
> >
> >
> > Since our dataset is very large, this procedure would take a lot of
> > time. Our question is basically whether it is possible to estimate
> a
> 
=== message truncated ===




 
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