[Limdep Nlogit List] MNL Dummy Variable issues
contactemt
contactemt at bigfoot.com
Fri Oct 13 20:44:52 EST 2006
Hi,
I have understood what ASC are now and realise they are not applicable to my
model :)
My model deals with generic choices - Greene uses the term "unlabeled" -
with the further complication that the choice set size can vary. I have
looked at his book and he describes the problem with unlabeled and ASC's in
Appendix 10A.
Coincidentally, in his example he uses gender as a non varying parameter
(within sets) as I did.
However, to get around the lack of ASC's he uses a pre defined utility model
and combines it with one of the utility variables. Why he chooses a
particular variable I don't know - and what is TTgen anyway (if you have the
book).
My variables are purely measured items - I do not wish to apply utility
constraints to them. I want the data to describe the model. Any utility
constructs I place on the model would be arbitrary. So is there another
approach I can use?
But if not, say I do try to use interactions to include categories - or
SDC's as he calls them:
How should I choose the variable(s) to interact with?
Should I choose 1 or many?
Should I only include the variable interaction term or should I include it
by itself also - what would be the point as they would be collinear wouldn't
they?
How should I encode gender?
If I choose 1,0 then half the interactions would be 0.
If I choose 1,-1 will NLOGIT be able to fit the =ve and -ve values of the
interacting term(s) OK?
If I have more categories/dummy variables to add to the model, do I need a
set of interactions for each one or can I combine them?
I have searched and not been able to find any examples of the type of
unlabeled model I wish to run, and I'm afraid I don't have the ability to
extrapolate from the exclusively "labeled" models out there.
Any further help appreciated.
> You get this because the default in NLOGIT is to fit alternative specific
> constants (when you use the ONE term) and you have 30 alternatives in the
> choice
> set. Add to that the interaction between gender you requested in RHS2 and
> your
> one generic attribute (pRF1) you get 59 parameters. Several of these
> parameters
> are fixed which means you have very low frequencies for them or one level
> of
> gender never choice that specific alternative.
>
> Perhaps you should read about choice modeling in Greene's book Applied
> Choice
> Analysis. He discusses all these issues and the defaults of NLOGIT.
>
> Tom
>
> -----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 1:29 PM
> To: Limdep and Nlogit Mailing List
> Subject: Re: [Limdep Nlogit List] MNL Dummy Variable issues
>
> Thanks,
>
> After a quick look it seems the Limdep rh2 variable is used for this.
> So I have:
>
> NLOGIT ; Lhs = CHOICE, SETSIZE
> ; Rhs = v
> ; Rh2 = One, GENDER
> ; Prob = probs $
>
>
> Incidentally, (I havent read through the issues but)
> I run a very simple (one attribute)
>
> NLOGIT ; Lhs = CHOICE, SETSIZE
> ; Rhs = pRF1
> ; Rh2 = One, GENDER
> ; Prob = probs $
>
>
> as a test and get 59 parameters. Why is this?
>
> Sorry if a stupid Q.
> +
> | Discrete choice (multinomial logit) model |
> | Maximum Likelihood Estimates |
> | Model estimated: Oct 12, 2006 at 06:14:54PM.|
> | Dependent variable Choice |
> | Weighting variable None |
> | Number of observations 1704 |
> | Iterations completed 18 |
> | Log likelihood function -.3005320E-08 |
> | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj |
> | No coefficients -5795.6403 1.00000 1.00000 |
> | Constants only. Must be computed directly. |
> | Use NLOGIT ;...; RHS=ONE $ |
> | Response data are given as ind. choice. |
> | Number of obs.= 1704, skipped 0 bad obs. |
> +---------------------------------------------+
>
>
> |+---------+--------------+----------------+--------+---------+
> |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] |
> +---------+--------------+----------------+--------+---------+
> PRF1 .28562667 1100.58000 .000 .9998
> A_Alt.1 121.889077 ......(Fixed Parameter).......
> AltxHCA1 -86.8291397 47685.8780 -.002 .9985
> A_Alt.2 -17.6981970 50283.9113 .000 .9997
> AltxHCA2 11.4289719 38053.7076 .000 .9998
> A_Alt.3 -15.6341676 28185.0248 -.001 .9996
> AltxHCA3 9.45993004 23720.4712 .000 .9997
> A_Alt.4 -14.0208822 18985.8265 -.001 .9994
> AltxHCA4 7.93083927 14502.9551 .001 .9996
> A_Alt.5 -13.7822969 ......(Fixed Parameter).......
> AltxHCA5 7.88816799 ......(Fixed Parameter).......
> A_Alt.6 -12.9643251 ......(Fixed Parameter).......
> AltxHCA6 7.03157581 431.216215 .016 .9870
> A_Alt.7 -11.5834031 2324.85755 -.005 .9960
> AltxHCA7 5.80855146 1886.76721 .003 .9975
> A_Alt.8 -9.82066678 ......(Fixed Parameter).......
> AltxHCA8 4.04330766 271.466257 .015 .9881
> A_Alt.9 -8.15221444 48.3176779 -.169 .8660
> AltxHCA9 2.65183332 31.0102930 .086 .9319
> A_Alt.10 -6.50502468 24.7267852 -.263 .7925
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.11 -5.06016269 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.12 -4.04467058 .00742264 -544.910 .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.13 -3.22120224 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.14 -2.70256143 .202507D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.15 -2.37109609 .211547D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.16 -2.16550807 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.17 -2.01944461 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.18 -1.97037254 .217681D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.19 -1.98283571 .242113D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.20 -2.04980440 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.21 -2.13493369 .113446D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.22 -2.14340813 .125961D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.23 -2.14440660 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.24 -2.18364464 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.25 -2.23302643 .174142D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.26 -2.26795137 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.27 -2.29960229 ......(Fixed Parameter).......
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.28 -2.29869412 .153029D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
> A_Alt.29 -2.30517803 .174283D-04 ******** .0000
> AltxHCA* 7.33600806 .02888315 253.989 .0000
>
>
>
>>
> That is exactly right: anything constant across the choice set
> needs to be put in as an interaction effect, via multiplying
> with with non-constant quantities. That way, you are in effect
> estimating two coefficients -- assuming you are assessing the effect
> of a dummy variable -- for each of those other (non-contant)
> quantities. To keep with your original example, you'd be getting a
> set of "male coefficients" and "female coefficients" for each of the
> non-constant variables with which you're interacting. Note that this
> would only be estimated *across* choice sets, since each individual
> is, presumably, constant in gender, so the gender "variable" never
> varies within any one choice set. [You should be careful that you
> don't have a small proportion of either zeros or ones in your dummy
> variable, or you may wind up not having enough cases to estimate the
> gender difference in coefficients. You might also consider some form
> of hierarchical modeling, particularly hierarchical Bayes.]
>
> FF
>
> Quoting "Thomas C. Eagle" <teagle at tceagle.com>:
>
>> You have to interact the category variables with alternative
> specific
>> constants,
>> much like you do with socio-demographic effects.
>>
>> Tom
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