[Limdep Nlogit List] MNL Dummy Variable issues
Thomas C. Eagle
teagle at tceagle.com
Fri Oct 13 04:11:16 EST 2006
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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