[Limdep Nlogit List] Exploded and negative wtp estimates
medard kakuru
medakseth at gmail.com
Sat Oct 19 20:11:09 AEDT 2024
Dear All,
Here is the syntax and the output of the generalized mixed logit model in
preference space, using MNL starting values. As you can see, the wtp
estimates are in hundreds of thousand yet my price attribute levels are in
thousands. When I add "userp" (using ML starting values) to the syntax, I
get reasonable wtp estimates (some in hundreds others in thousands) but
three out five are negative and the standard deviations for all are
extremely big- in hundreds of thousand.
Please advise.
CREATE ; nprice = -price $ ? make the price attribute negative
|-> calc ; ran(10000) $ ?seeding
|-> GMXLOGIT
; Lhs = choice ; Choices = A, B, C
; Rhs = fortific,weight,certific,twosezon,manysezo,nprice
;Pds = 4
;gmx
;tau=0.1
;gamma=0.1
;halton ;pts=100
; corr ;Parameters
; Fcn = fortific(n),weight(n),certific(n),twosezon(n),manysezo(n),
nprice(n)
; WTP =
fortific/nprice,weight/nprice,certific/nprice,twosezon/nprice,manysezo/nprice
$ ? estimate individual-specific wtp estimate
Iterative procedure has converged
Normal exit: 6 iterations. Status=0, F= .9000622D+03
-----------------------------------------------------------------------------
Start values obtained using MNL model
Dependent variable Choice
Log likelihood function -900.06222
Estimation based on N = 1076, K = 6
Inf.Cr.AIC = 1812.1 AIC/N = 1.684
---------------------------------------
Log likelihood R-sqrd R2Adj
Constants only -985.1678 .0864 .0739
Note: R-sqrd = 1 - logL/Logl(constants)
Warning: Model does not contain a full
set of ASCs. R-sqrd is problematic. Use
model setup with ;RHS=one to get LogL0.
---------------------------------------
Response data are given as ind. choices
Number of obs.= 1076, skipped 0 obs
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
CHOICE| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
FORTIFIC| .32357*** .08072 4.01 .0001 .16537 .48178
WEIGHT| -.27733*** .09413 -2.95 .0032 -.46182 -.09285
CERTIFIC| 1.60380*** .11305 14.19 .0000 1.38223 1.82536
TWOSEZON| .78121*** .12545 6.23 .0000 .53533 1.02708
MANYSEZO| 1.44881*** .11970 12.10 .0000 1.21419 1.68342
NPRICE|-.61331D-04* .3452D-04 -1.78 .0756 -.12899D-03 .63316D-05
--------+--------------------------------------------------------------------
nnnnn.D-xx or D+xx => multiply by 10 to -xx or +xx.
***, **, * ==> Significance at 1%, 5%, 10% level.
Model was estimated on Oct 19, 2024 at 11:49:37 AM
-----------------------------------------------------------------------------
Line search at iteration 53 does not improve the function
Exiting optimization
-----------------------------------------------------------------------------
Generalized Mixed (RP) Logit Model
Dependent variable CHOICE
Log likelihood function -826.91886
Restricted log likelihood -1182.10682
Chi squared [ 29](P= .000) 710.37593
Significance level .00000
McFadden Pseudo R-squared .3004703
Estimation based on N = 1076, K = 29
Inf.Cr.AIC = 1711.8 AIC/N = 1.591
---------------------------------------
Log likelihood R-sqrd R2Adj
No coefficients -1182.1068 .3005 .2909
Constants only -985.1678 .1606 .1492
At start values -899.3489 .0805 .0680
Note: R-sqrd = 1 - logL/Logl(constants)
Warning: Model does not contain a full
set of ASCs. R-sqrd is problematic. Use
model setup with ;RHS=one to get LogL0.
---------------------------------------
Response data are given as ind. choices
Replications for simulated probs. = 100
Used Halton sequences in simulations.
RPL model with panel has 269 groups
Fixed number of obsrvs./group= 4
BHHH estimator used for asymp. variance
Number of obs.= 1076, skipped 0 obs
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
CHOICE| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
|Random parameters in utility
functions..............................
FORTIFIC| 1.50696*** .54611 2.76 .0058 .43662 2.57731
WEIGHT| -.20769 .56298 -.37 .7122 -1.31111 .89573
CERTIFIC| 9.07085*** 1.59825 5.68 .0000 5.93834 12.20335
TWOSEZON| 5.26384*** 1.05771 4.98 .0000 3.19076 7.33692
MANYSEZO| 8.40225*** 1.58085 5.32 .0000 5.30384 11.50067
NPRICE| .00063*** .00024 2.63 .0086 .00016 .00110
|Diagonal values in Cholesky matrix,
L...............................
NsFORTIF| 4.97379*** 1.17328 4.24 .0000 2.67420 7.27337
NsWEIGHT| .41995 1.33312 .32 .7528 -2.19292 3.03281
NsCERTIF| 4.06231*** 1.47632 2.75 .0059 1.16877 6.95584
NsTWOSEZ| 5.15553*** 1.34628 3.83 .0001 2.51686 7.79420
NsMANYSE| 1.40139 1.40002 1.00 .3168 -1.34259 4.14538
NsNPRICE| .00101*** .00029 3.54 .0004 .00045 .00157
|Below diagonal values in L matrix. V =
L*Lt.........................
WEIG:FOR| -1.48938* .88668 -1.68 .0930 -3.22725 .24849
CERT:FOR| -2.51743 1.59438 -1.58 .1143 -5.64236 .60749
CERT:WEI| 4.19613** 1.80622 2.32 .0202 .65600 7.73626
TWOS:FOR| 2.95810*** 1.06047 2.79 .0053 .87962 5.03659
TWOS:WEI| .95649 1.46272 .65 .5132 -1.91039 3.82337
TWOS:CER| -2.25893* 1.29302 -1.75 .0806 -4.79321 .27535
MANY:FOR| -.32904 1.13783 -.29 .7724 -2.55915 1.90107
MANY:WEI| 3.19079* 1.85861 1.72 .0860 -.45202 6.83360
MANY:CER| -6.35832*** 1.52035 -4.18 .0000 -9.33814 -3.37849
MANY:TWO| -7.79234*** 1.74477 -4.47 .0000 -11.21203 -4.37265
NPRI:FOR| -.00020 .00025 -.80 .4223 -.00068 .00029
NPRI:WEI| .00121** .00047 2.57 .0101 .00029 .00214
NPRI:CER| -.00072* .00042 -1.69 .0908 -.00155 .00011
NPRI:TWO| -.00051 .00037 -1.37 .1714 -.00123 .00022
NPRI:MAN| .00027 .00043 .62 .5331 -.00058 .00112
|Covariances of Random
Parameters....................................
TauScale| 1.13115*** .10543 10.73 .0000 .92452 1.33779
|Weighting parameter gamma in GMX
model..............................
GammaMXL| -.22613** .09949 -2.27 .0230 -.42113 -.03113
| Sample Mean Sample
Std.Dev.....................................
Sigma(i)| .97097 1.32745 .73 .4645 -1.63079 3.57272
|Standard deviations of parameter
distributions......................
sdFORTIF| 4.97379*** 1.17328 4.24 .0000 2.67420 7.27337
sdWEIGHT| 1.54746 .97110 1.59 .1110 -.35587 3.45078
sdCERTIF| 6.35982*** 1.75250 3.63 .0003 2.92499 9.79465
sdTWOSEZ| 6.43020*** 1.24563 5.16 .0000 3.98881 8.87159
sdMANYSE| 10.6490*** 1.97084 5.40 .0000 6.7863 14.5118
sdNPRICE| .00184*** .00036 5.15 .0000 .00114 .00253
|Covariances of Random
Parameters....................................
WEIG:FOR| -7.40788 5.39137 -1.37 .1694 -17.97476 3.15901
CERT:FOR| -12.5212 10.38866 -1.21 .2281 -32.8826 7.8402
CERT:WEI| 5.51158 7.31301 .75 .4510 -8.82166 19.84481
TWOS:FOR| 14.7130* 7.61270 1.93 .0533 -.2076 29.6336
TWOS:WEI| -4.00408 4.20688 -.95 .3412 -12.24941 4.24125
TWOS:CER| -12.6097 9.77976 -1.29 .1973 -31.7777 6.5582
MANY:FOR| -1.63656 5.54775 -.29 .7680 -12.50996 9.23684
MANY:WEI| 1.83003 5.29230 .35 .7295 -8.54268 12.20274
MANY:CER| -11.6121 11.85646 -.98 .3274 -34.8504 11.6261
MANY:TWO| -23.7320* 13.89019 -1.71 .0875 -50.9563 3.4923
NPRI:FOR| -.00099 .00132 -.75 .4552 -.00358 .00161
NPRI:WEI| .00080 .00187 .43 .6677 -.00287 .00448
NPRI:CER| .00267 .00449 .60 .5518 -.00612 .01146
NPRI:TWO| -.00042 .00295 -.14 .8871 -.00620 .00536
NPRI:MAN| .01282** .00637 2.01 .0443 .00033 .02531
--------+--------------------------------------------------------------------
***, **, * ==> Significance at 1%, 5%, 10% level.
Model was estimated on Oct 19, 2024 at 11:54:35 AM
-----------------------------------------------------------------------------
--------+-----------------------------------------------------
Cor.Mat.|FORTIFIC WEIGHT CERTIFIC TWOSEZON MANYSEZO NPRICE
--------+-----------------------------------------------------
FORTIFIC| 1.00000 -.96247 -.39583 .46003 -.03090 -.10825
WEIGHT| -.96247 1.00000 .56003 -.40240 .11105 .28338
CERTIFIC| -.39583 .56003 1.00000 -.30834 -.17146 .22877
TWOSEZON| .46003 -.40240 -.30834 1.00000 -.34658 -.03546
MANYSEZO| -.03090 .11105 -.17146 -.34658 1.00000 .65585
NPRICE| -.10825 .28338 .22877 -.03546 .65585 1.00000
Saved Individual Estimates of WTP in matrix WTP_I [ 269x5]
Alternative Attribute Income/Cost
Chosen FORTIFIC NPRICE
Chosen WEIGHT NPRICE
Chosen CERTIFIC NPRICE
Chosen TWOSEZON NPRICE
Chosen MANYSEZO NPRICE
(Saved absolute values. Check signs of coefficients.)
|-> MATRIX ; List ; 1/269*1'wtp_i $ ?mean wtp
RESULT| 1 2 3 4
5
--------+----------------------------------------------------------------------
1| -208387. 63355.3 -155431. -109576.
-33803.6
On Fri, Oct 18, 2024 at 6:11 PM Arthur Caplan via Limdep <
limdep at mailman.sydney.edu.au> wrote:
> Hello Medard,
>
> It might help if you shared the code you are using.
>
> Arthur Caplan
>
>
> Department of Applied Economics
> Utah State University
> 4835 Old Main Hill
> Logan, Utah 84322-4835
> tel: 435-797-0775
> web: https://url.au.m.mimecastprotect.com/s/00ZpCWLVXkUyjjrAYC6f1FoZt0k?domain=sites.google.com
> ________________________________
> From: Limdep <limdep-bounces at mailman.sydney.edu.au> on behalf of medard
> kakuru via Limdep <limdep at mailman.sydney.edu.au>
> Sent: Thursday, October 17, 2024 11:58 PM
> To: Limdep and Nlogit Mailing List <limdep at mailman.sydney.edu.au>
> Cc: medard kakuru <medakseth at gmail.com>
> Subject: Re: [Limdep Nlogit List] Exploded and negative wtp estimates
>
> Greetings to you all!
> I estimated wtp using three models: RPL, Generalised mixed logit in
> preference space and in wtp space. My estimates are extremely big - I have
> tried the normal, triangular and log normal distributions. They become
> bigger if I use more than 100 random draws, halton draws don't make them
> any better. Secondly, they are negative yet the MNL estimates are positive
> and my price parameter is negative. What could be the reason/problem for
> negative and explosive wtp estimates?
>
>
> Best regards,
>
> Medard
>
> >
> >
> >
> _______________________________________________
> Limdep site list
> Limdep at mailman.sydney.edu.au
> http://limdep.itls.usyd.edu.au
>
> _______________________________________________
> Limdep site list
> Limdep at mailman.sydney.edu.au
> http://limdep.itls.usyd.edu.au
>
>
More information about the Limdep
mailing list