Hi Guys. OK, i finally figure out how to make post organized!
i have one question regarding the multinomial logit regression. Below you can first find my model and then the empirical result. you can see for i.wsd variable (the dummy variable with 0 and 1), the coefficient for outcome 3 is negative and it is significant at 90% level (some may say it is not a significant level anymore). this means (if i am right) compared with outcome 3, with the wsd changes from 0 to 1, the probability of outcome 1 becomes bigger while the probability of outcome 3 becomes smaller. However, when i use the "mtabel" command to calculate the predicted probability, i find the probability of outcome 3 increase, though only a little bit. I am really confused about how to explain this. Is there anything wrong? i find this post but don't think it is very relevant to the question here: https://www.statalist.org/forums/for...etation-method. Thanks in advance.
Code:
mlogit pi ternorv ternorc cumu10mid politydif i.wsd powdif salience leaage oppleaage i.milnoncombat i.combat i.rebel bsuc_sum rsuc_sum tsuc_sum bsuc_sum_opp rsuc_sum_opp tsuc_sum_opp, cluster(ccode) base(1)
Multinomial logistic regression                 Number of obs     =        261
                                                Wald chi2(36)     =     270.86
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -248.98639               Pseudo R2         =     0.1185

                                   (Std. Err. adjusted for 78 clusters in ccode)
--------------------------------------------------------------------------------
               |               Robust
            pi |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
1              |  (base outcome)
---------------+----------------------------------------------------------------
2              |
       ternorv |   .2401438   .5514606     0.44   0.663    -.8406992    1.320987
       ternorc |   .1888044   .4593973     0.41   0.681    -.7115979    1.089207
     cumu10mid |  -.0344206   .0909131    -0.38   0.705    -.2126071    .1437658
     politydif |   .0150453   .0309368     0.49   0.627    -.0455897    .0756802
         1.wsd |  -1.230385    .400907    -3.07   0.002    -2.016149    -.444622
        powdif |  -.5573728   5.237646    -0.11   0.915    -10.82297    9.708224
      salience |  -.0212136    .131619    -0.16   0.872     -.279182    .2367548
        leaage |  -.0000551   .0000854    -0.64   0.519    -.0002225    .0001124
     oppleaage |  -.0000478   .0000865    -0.55   0.580    -.0002173    .0001216
1.milnoncombat |  -.7916929   .5950595    -1.33   0.183    -1.957988    .3746023
      1.combat |  -.3480807   .4898449    -0.71   0.477    -1.308159    .6119975
       1.rebel |  -1.026594   .3696071    -2.78   0.005    -1.751011   -.3021775
      bsuc_sum |  -.4019039   .1352459    -2.97   0.003     -.666981   -.1368268
      rsuc_sum |  -.1221266   .2577125    -0.47   0.636    -.6272338    .3829806
      tsuc_sum |   .1642505    .361909     0.45   0.650    -.5450782    .8735791
  bsuc_sum_opp |  -.7478829   .5317541    -1.41   0.160    -1.790102    .2943359
  rsuc_sum_opp |   .6970344   .4731081     1.47   0.141    -.2302404    1.624309
  tsuc_sum_opp |   .2248461   .6275107     0.36   0.720    -1.005052    1.454744
         _cons |   1.987506   1.528682     1.30   0.194    -1.008656    4.983668
---------------+----------------------------------------------------------------
3              |
       ternorv |    .568666   .5244716     1.08   0.278    -.4592795    1.596611
       ternorc |   .0261033   .3941644     0.07   0.947    -.7464448    .7986513
     cumu10mid |   .1036039   .1016358     1.02   0.308    -.0955985    .3028063
     politydif |  -.0107027   .0412228    -0.26   0.795    -.0914979    .0700925
         1.wsd |  -.7415701   .4303823    -1.72   0.085    -1.585104    .1019637
        powdif |  -2.601234   6.200299    -0.42   0.675     -14.7536    9.551129
      salience |   .0068077   .1127426     0.06   0.952    -.2141637    .2277792
        leaage |  -.0001791   .0001116    -1.61   0.108    -.0003978    .0000396
     oppleaage |    .000015   .0000939     0.16   0.873     -.000169    .0001989
1.milnoncombat |  -1.273212   .5989447    -2.13   0.034    -2.447122   -.0993023
      1.combat |  -.2542086   .4880725    -0.52   0.602    -1.210813    .7023959
       1.rebel |  -.9614701   .4152078    -2.32   0.021    -1.775262   -.1476779
      bsuc_sum |  -.9121091   .3425758    -2.66   0.008    -1.583545   -.2406729
      rsuc_sum |   .3445298   .3724609     0.93   0.355    -.3854802     1.07454
      tsuc_sum |   .2300749   .4050782     0.57   0.570    -.5638637    1.024014
  bsuc_sum_opp |   .2071136   .5183295     0.40   0.689    -.8087934    1.223021
  rsuc_sum_opp |  -.0990741    .539271    -0.18   0.854    -1.156026    .9578777
  tsuc_sum_opp |  -.3641798   .6610856    -0.55   0.582    -1.659884    .9315241
         _cons |   1.100974   1.507687     0.73   0.465    -1.854039    4.055986
--------------------------------------------------------------------------------
mtable, at(wsd=(0 1) milnoncombat=0 combat=0 rebel=0 ) atmeans outcome (1 3) statistics(ci) dec(2)

Expression: Pr(pi), predict(outcome())

           |      wsd         1         3
 ----------+-----------------------------
     Pr(y) |        0      0.08      0.36
        ll |        0      0.02      0.24
        ul |        0      0.14      0.49
     Pr(y) |        1      0.19      0.42
        ll |        1      0.08      0.26
        ul |        1      0.29      0.58