[Limdep Nlogit List] Interpretation of the alternative specific constants (alt1, alt2)

[Harold Mayaba]

Hi Brett, 
Thank you for the quick response. The model ran fine with no errors, so the model is not unidentified. I used unlabelled choice alternatives. I have the results for the means of caged(n) ,acdoors(n); I did not include the results in my previous post because I just wanted to concentrate on the areas I need help with. 

Regards
Harold


 |Heterogeneityin mean, Parameter:Variable...........................

CAGE:FEM|    .12655         .31811     .40  .6908     -.49693    .75003

CAGE:MAL|   -.33358         .29081    -1.15 .2514     -.90355    .23639

CAGE:AGE|    .02443         .02390    1.02  .3067     -.02241    .07126

CAG0:AGE|    .02313*        .01212    1.91  .0563     -.00062    .04688

CAG1:AGE|    .00748         .00921     .81  .4167     -.01057    .02553

CAG2:AGE|    .01783**       .00829    2.15  .0314      .00159    .03408

CAG3:AGE|    .01035         .00855    1.21  .2258     -.00640    .02710

CAGE:INC|    .00609         .09467     .06  .9487     -.17946    .19163

CAG0:INC|   -.02964*        .01772    -1.67 .0944     -.06438    .00509

CAG1:INC|    .00393         .00932     .42  .6734     -.01433    .02219

CAG2:INC|    .02134***      .00752     2.84 .0045      .00660    .03607

CAG3:INC|    .00236         .00523     .45  .6525     -.00790    .01262

ACDO:FEM|  -3.53830***      .58943    -6.00 .0000    -4.69357  -2.38303

ACDO:MAL|  -3.12750***      .49045    -6.38 .0000    -4.08875  -2.16624

ACDO:AGE|   -.03362         .02422    -1.39 .1651     -.08110    .01386

ACD0:AGE|   -.02344         .01805    -1.30 .1941     -.05881    .01193

ACD1:AGE|    .02595**       .01258    2.06  .0392      .00129    .05062

ACD2:AGE|    .02931***      .00996     2.94 .0032      .00979    .04883

ACD3:AGE|    .00262         .00904     .29  .7722     -.01510    .02033

ACDO:INC|   -.24572*        .13028    -1.89 .0593     -.50106    .00961

ACD0:INC|    .01503         .02336     .64  .5201     -.03076    .06081

ACD1:INC|    .00998         .01776     .56  .5743     -.02484    .04480

ACD2:INC|    .03218**       .01452    2.22  .0267      .00372    .06064

ACD3:INC|    .00153         .00884     .17  .8624     -.01579    .01886



Harold

My guess is that your variable None=1 and that has led to the model being unidentified. Or you are choices are actually labelled and caged and acdoors  are your alternate specific constants.

One final comment the code has the conditional means of caged(n) ,acdoors(n),
As a function of male and female? Unidentified
All age groups? Unidentified
All incomes? Unidentified.

Include male only
Convert your age and incomes to a scale (i.e. use the mid points of the brackets)