[Limdep Nlogit List] Same predictors in both regression equation and probability equation

Cem Payasli cem.payasli at emu.edu.tr
Wed Feb 15 20:20:41 AEDT 2017


Dear All,

I have fitted the following specification for negative binomial latent class model with 2 classes.

My questions:

 1) Is it possible to compare several non-nested alternatives on the basis of AIC or log-likelihood ?

 2)   How can I impose restrictions on both mean(regression) part and class membership probability part ?

  3) What would be the interpretation of second class variables with fixed values always set to "0"  ?

   4) In limdep manual LR test example is provided so the restriction of  extended LC (predictors as class probablities) versus constant probablities ( no predictors as class probablities) is given. In th example below Asian variable is signficant in mean regression models but insignficant as class probablity predictor. Supoosing it were signficant there as well,  could we say it better belongs to eithe mean part of probability again ?

Thanks. for any suggestion.
Cem Payaslıoğlu
Department of Economics
Eastern Mediterranean University, Magusa,TRNC
Mersin 10 Turkey

Latent Class / Panel NegBnReg Model
Dependent variable               PATENT
Log likelihood function     -2772.13293
Restricted log likelihood -386994.81568
Chi squared [  15 d.f.]    768445.36551
Significance level               .00000
McFadden Pseudo R-squared      .9928368
Estimation based on N =    566, K =  21
Inf.Cr.AIC  =   5586.3 AIC/N =    9.870
Model estimated: Feb 14, 2017, 20:49:28
Sample is  1 pds and    566 individuals
Negative binomial regression model
--------+--------------------------------------------------------------------
        |                  Standard            Prob.      95% Confidence
  PATENT|  Coefficient       Error       z    |z|>Z*         Interval
--------+--------------------------------------------------------------------
        |Model parameters for latent class 1
     LRD|     .42219***      .07490     5.64  .0000      .27540    .56899
  LSALES|     .27586*        .14920     1.85  .0645     -.01656    .56828
  LCAPEX|    -.20480*        .11319    -1.81  .0704     -.42664    .01705
      R1|     .88042         .70745     1.24  .2133     -.50616   2.26700
      R2|    1.22297*        .73600     1.66  .0966     -.21955   2.66550
      R3|    1.36867*        .72247     1.89  .0582     -.04735   2.78469
      R4|    1.32462*        .69588     1.90  .0570     -.03929   2.68853
   ASIAN|     .50393***      .17740     2.84  .0045      .15624    .85163
   Alpha|    1.19664***      .19265     6.21  .0000      .81906   1.57422
        |Model parameters for latent class 2
     LRD|     .21053***      .05043     4.17  .0000      .11169    .30938
  LSALES|     .09020         .05760     1.57  .1174     -.02269    .20310
  LCAPEX|    -.02938         .05241     -.56  .5751     -.13210    .07334
      R1|    1.48180***      .30280     4.89  .0000      .88832   2.07528
      R2|    2.17540***      .37662     5.78  .0000     1.43724   2.91356
      R3|    1.77508***      .25976     6.83  .0000     1.26596   2.28419
      R4|    1.50124***      .27273     5.50  .0000      .96670   2.03578
   ASIAN|     .19233**       .07907     2.43  .0150      .03735    .34732
   Alpha|    5.12409***      .98185     5.22  .0000     3.19970   7.04849
        |Estimated prior probabilities for class membership
   ONE_1|   -8.52758***     1.45420    -5.86  .0000   -11.37775  -5.67741
LSALES_1|     .95047***      .16183     5.87  .0000      .63329   1.26766
 ASIAN_1|     .64236*        .35511     1.81  .0705     -.05364   1.33836
   ONE_2|        0.0    .....(Fixed Parameter).....
LSALES_2|        0.0    .....(Fixed Parameter).....
 ASIAN_2|        0.0    .....(Fixed Parameter).....
--------+--------------------------------------------------------------------
Note: ***, **, * ==>  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




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