[Limdep Nlogit List] Finding confidence intervals of WTA after LCRPLOGIT

Kolady, Deepthi Elizabeth P deepthi.kolady at okstate.edu
Thu Mar 28 00:30:50 AEDT 2024


Hello Dr.Greene,

I use discrete choice data to estimate latent class-wise willingness to accept (WTA) values. I am using the LCRPLOGIT command because I would like to combine the random parameter logit model with the latent class model to account for heterogeneity. I am using the LCM command to use a variable of interest to determine class probability (in my case, the climate change concern scale).

I have attached my model estimation and results. I used this result to estimate class-wise WTA values. Can I estimate the confidence interval for WTA values using Krinsky and Robb's method after LCRPLOGIT estimation?
I can generate a K-density plot and show the distribution of class-wise or individual WTA, but as a reviewer asked, I need help figuring out how to get confidence intervals to report.

Thank you in advance for your help.

Sincerely,
Deepthi



-------------- next part --------------

|-> IMPORT;FILE="C:\Users\amrit\OneDrive\Desktop\Pramisha\data LCM (3).csv"$
Last observation read from data file was    6858
|-> LCRPLOGIT ; Lhs = yvec
    ; Choices = 1,2,3
    ; Rhs = price, none, constont, convtoct, convtont, noctocc,tencon, fivecon, nonprofi, private, governme
    ; Pds = 6
    ; Rpl ; Fcn = none (n), constont (n), convtoct (n), convtont (n), noctocc (n),tencon (n), fivecon (n), nonprofi (n), private (n), governme (n); Draws = 20 ; Halton
    ; LCM =ccscl; Pts = 2$

+------------------------------------------------------+
|WARNING:   Bad observations were found in the sample. |
|Found   41 bad observations among   2286 individuals. |
|You can use ;CheckData to get a list of these points. |
+------------------------------------------------------+

Iterative procedure has converged
Normal exit:   5 iterations. Status=0, F=    .2295341D+04

-----------------------------------------------------------------------------
Start values obtained using MNL model
Dependent variable               Choice
Log likelihood function     -2295.34134
Estimation based on N =   2245, K =  11
Inf.Cr.AIC  =   4612.7 AIC/N =    2.055
---------------------------------------
            Log likelihood R-sqrd R2Adj
Constants only  -2390.1683  .0397 .0306
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.=  2286, skipped   41 obs
--------+--------------------------------------------------------------------
        |                  Standard            Prob.      95% Confidence
    YVEC|  Coefficient       Error       z    |z|>Z*         Interval
--------+--------------------------------------------------------------------
    NONE|     .98391***      .13597     7.24  .0000      .71741   1.25041
CONSTONT|    -.42031***      .10316    -4.07  .0000     -.62251   -.21812
CONVTOCT|    -.43842***      .10341    -4.24  .0000     -.64109   -.23574
CONVTONT|    -.52237***      .10362    -5.04  .0000     -.72546   -.31927
 NOCTOCC|    -.07759         .07115    -1.09  .2755     -.21705    .06187
  TENCON|    -.63616***      .08483    -7.50  .0000     -.80242   -.46991
 FIVECON|    -.21314**       .08380    -2.54  .0110     -.37739   -.04888
NONPROFI|     .36301**       .14279     2.54  .0110      .08314    .64287
 PRIVATE|     .29432*        .16079     1.83  .0672     -.02082    .60946
GOVERNME|     .52775***      .14949     3.53  .0004      .23475    .82074
   PRICE|     .06289***      .00731     8.60  .0000      .04855    .07723
--------+--------------------------------------------------------------------
***, **, * ==>  Significance at 1%, 5%, 10% level.
Model was estimated on Feb 20, 2024 at 09:07:09 AM
-----------------------------------------------------------------------------

Line search at iteration 37 does not improve the function
Exiting optimization

-----------------------------------------------------------------------------
Latent Class Mixed (RP) Logit Model
Dependent variable                 YVEC
Log likelihood function     -1988.56101
Restricted log likelihood   -2466.38459
Chi squared [ 42](P= .000)    955.64716
Significance level               .00000
McFadden Pseudo R-squared      .1937344
Estimation based on N =   2245, K =  42
Inf.Cr.AIC  =   4061.1 AIC/N =    1.809
---------------------------------------
            Log likelihood R-sqrd R2Adj
No coefficients -2466.3846  .1937 .1861
Constants only  -2390.1683  .1680 .1602
At start values -2287.0409  .1305 .1223
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. =  20
Used Halton sequences in simulations.
Number of latent classes =            2
Average Class Probabilities
     .492  .508
LCM model with panel has     381 groups
Fixed number of obsrvs./group=        6
Number of obs.=  2286, skipped   41 obs
--------+--------------------------------------------------------------------
        |                  Standard            Prob.      95% Confidence
    YVEC|  Coefficient       Error       z    |z|>Z*         Interval
--------+--------------------------------------------------------------------
        |This is THETA(01) in class probability model........................
  _ONE|1|    1.05658**       .43214     2.44  .0145      .20960   1.90357
_CCSCL|1|    -.36602***      .13780    -2.66  .0079     -.63610   -.09593
        |This is THETA(02) in class probability model........................
  _ONE|2|        0.0    .....(Fixed Parameter).....
_CCSCL|2|        0.0    .....(Fixed Parameter).....
--------+--------------------------------------------------------------------
***, **, * ==>  Significance at 1%, 5%, 10% level.
Fixed parameter ... is constrained to equal the value or
had a nonpositive st.error because of an earlier problem.
Model was estimated on Feb 20, 2024 at 09:09:19 AM
-----------------------------------------------------------------------------


-----------------------------------------------------------------------------
Mixed Logit Model Random Utility  Class  1
--------+--------------------------------------------------------------------
        |                  Standard            Prob.      95% Confidence
    YVEC|  Coefficient       Error       z    |z|>Z*         Interval
--------+--------------------------------------------------------------------
        |Random parameters in utility functions..............................
    NONE|    1.79738***      .27210     6.61  .0000     1.26407   2.33070
CONSTONT|   -1.00916***      .24835    -4.06  .0000    -1.49591   -.52241
CONVTOCT|    -.87422***      .24285    -3.60  .0003    -1.35020   -.39824
CONVTONT|    -.71212***      .22827    -3.12  .0018    -1.15953   -.26471
 NOCTOCC|    -.18461         .17928    -1.03  .3031     -.53599    .16677
  TENCON|   -1.31894***      .23823    -5.54  .0000    -1.78585   -.85202
 FIVECON|    -.47191**       .20490    -2.30  .0213     -.87350   -.07032
NONPROFI|    -.24968         .34279     -.73  .4664     -.92154    .42219
 PRIVATE|    -.43424         .40523    -1.07  .2839    -1.22847    .36000
GOVERNME|     .03432         .36532      .09  .9251     -.68170    .75035
        |Nonrandom parameters in utility functions...........................
   PRICE|     .09847***      .01787     5.51  .0000      .06345    .13349
        |Distns. of RPs. Std.Devs or limits of triangular....................
  NsNONE|     .00332         .07391      .04  .9641     -.14154    .14818
NsCONSTO|     .02130         .15127      .14  .8880     -.27517    .31778
NsCONVTO|     .00781         .09594      .08  .9351     -.18024    .19585
NsCONVTO|     .00781         .09594      .08  .9351     -.18024    .19585
NsNOCTOC|     .00145         .10217      .01  .9887     -.19880    .20169
NsTENCON|     .01758         .15828      .11  .9116     -.29265    .32781
NsFIVECO|     .01025         .12718      .08  .9358     -.23902    .25951
NsNONPRO|     .00012         .12589      .00  .9992     -.24662    .24686
NsPRIVAT|     .01667         .14701      .11  .9097     -.27147    .30480
NsGOVERN|     .02246         .11014      .20  .8385     -.19342    .23834
--------+--------------------------------------------------------------------
***, **, * ==>  Significance at 1%, 5%, 10% level.
Model was estimated on Feb 20, 2024 at 09:09:19 AM
-----------------------------------------------------------------------------


-----------------------------------------------------------------------------
Mixed Logit Model Random Utility  Class  2
--------+--------------------------------------------------------------------
        |                  Standard            Prob.      95% Confidence
    YVEC|  Coefficient       Error       z    |z|>Z*         Interval
--------+--------------------------------------------------------------------
        |Random parameters in utility functions..............................
    NONE|     .21222         .21046     1.01  .3133     -.20027    .62471
CONSTONT|    -.14869         .13920    -1.07  .2854     -.42152    .12414
CONVTOCT|    -.13854         .13775    -1.01  .3145     -.40853    .13144
CONVTONT|    -.23595*        .14128    -1.67  .0949     -.51286    .04097
 NOCTOCC|     .18049*        .09311     1.94  .0526     -.00200    .36299
  TENCON|    -.69544***      .11206    -6.21  .0000     -.91507   -.47581
 FIVECON|    -.39366***      .11470    -3.43  .0006     -.61847   -.16884
NONPROFI|     .67366***      .17124     3.93  .0001      .33803   1.00929
 PRIVATE|     .24390         .19592     1.24  .2132     -.14009    .62789
GOVERNME|     .61897***      .17562     3.52  .0004      .27476    .96318
        |Nonrandom parameters in utility functions...........................
   PRICE|     .10705***      .01257     8.52  .0000      .08241    .13169
        |Distns. of RPs. Std.Devs or limits of triangular....................
  NsNONE|     .00170         .08560      .02  .9842     -.16607    .16947
NsCONSTO|     .00989         .08743      .11  .9099     -.16146    .18124
NsCONVTO|     .00531         .06185      .09  .9316     -.11592    .12654
NsCONVTO|     .00531         .06185      .09  .9316     -.11592    .12654
NsNOCTOC|     .01575         .06649      .24  .8127     -.11457    .14607
NsTENCON|     .02666         .08303      .32  .7481     -.13608    .18941
NsFIVECO|     .01115         .08277      .13  .8929     -.15107    .17337
NsNONPRO|     .00483         .08282      .06  .9535     -.15749    .16715
NsPRIVAT|     .01847         .08589      .22  .8297     -.14987    .18682
NsGOVERN|     .03388         .07943      .43  .6697     -.12180    .18956
--------+--------------------------------------------------------------------
***, **, * ==>  Significance at 1%, 5%, 10% level.
Model was estimated on Feb 20, 2024 at 09:09:19 AM
-----------------------------------------------------------------------------



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