Dear Statalist,

I am using Stata 15.1 and want to estimate the marginal effects of an ordered logit model that has an outcome variable with three categories.
When running the model
Code:
xtologit insurance_type rain_previous WTP_group P_InsuranceC Female age2 Educ2 ///
i.InsuranceExp##i.InsuranceUnderstanding Trust_Company HHsize2 DepRate Crowding RCSI ///
FamilyRemittances DurationFarmer RainFed log_Yield AverProdSold AverDangerProdLoss ///
RiskAversion, vce(robust) nolog
it shows the following output:
HTML Code:
Random-effects ordered logistic regression      Number of obs     =        218
Group variable: HHid                            Number of groups  =         56

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          1
                                                              avg =        3.9
                                                              max =          5

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(23)     =      50.98
Log pseudolikelihood  = -185.80101              Prob > chi2       =     0.0007

                                                         (Std. Err. adjusted for 56 clusters in HHid)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                     insurance_type |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                      rain_previous |  -.0622835   .1704658    -0.37   0.715    -.3963903    .2718234
                          WTP_group |   4.129787   2.252134     1.83   0.067    -.2843142    8.543888
                       P_InsuranceC |  -.1946897   .5041385    -0.39   0.699    -1.182783    .7934037
                             Female |  -.6773839     .45346    -1.49   0.135    -1.566149    .2113813
                               age2 |  -.0001368   .0002462    -0.56   0.579    -.0006194    .0003458
                              Educ2 |  -.0168312   .0207194    -0.81   0.417    -.0574405    .0237782
                     1.InsuranceExp |   .6841136   1.052551     0.65   0.516    -1.378848    2.747075
                                    |
             InsuranceUnderstanding |
                                 2  |  -.3914879    1.11747    -0.35   0.726    -2.581688    1.798713
                                 3  |   .7297591   .9662533     0.76   0.450    -1.164063    2.623581
                                    |
InsuranceExp#InsuranceUnderstanding |
                               1 2  |   .4299672   1.260509     0.34   0.733    -2.040586     2.90052
                               1 3  |  -.1093954    1.09006    -0.10   0.920    -2.245873    2.027083
                                    |
                      Trust_Company |  -.0792627   .3887562    -0.20   0.838    -.8412109    .6826855
                            HHsize2 |   .0092903   .0036231     2.56   0.010     .0021892    .0163913
                            DepRate |   .0620267   .6466889     0.10   0.924     -1.20546    1.329514
                           Crowding |   .3460283   .3461374     1.00   0.317    -.3323885    1.024445
                               RCSI |   .2281215   .1541255     1.48   0.139     -.073959     .530202
                  FamilyRemittances |  -.1089542   .3493816    -0.31   0.755    -.7937295    .5758211
                     DurationFarmer |  -.0098795   .0386288    -0.26   0.798    -.0855906    .0658316
                            RainFed |   .3382859   .5544276     0.61   0.542    -.7483723    1.424944
                          log_Yield |  -.2135142   .1554528    -1.37   0.170    -.5181961    .0911678
                       AverProdSold |   .0096645   .0057879     1.67   0.095    -.0016795    .0210086
                 AverDangerProdLoss |  -.1618212   .1662221    -0.97   0.330    -.4876106    .1639681
                       RiskAversion |   .5916548   .4358455     1.36   0.175    -.2625866    1.445896
------------------------------------+----------------------------------------------------------------
                              /cut1 |   1.675089   2.847313                     -3.905542     7.25572
                              /cut2 |   4.032432   2.901428                     -1.654263    9.719127
------------------------------------+----------------------------------------------------------------
                          /sigma2_u |   6.80e-32   1.25e-31                      1.83e-33    2.52e-30
-----------------------------------------------------------------------------------------------------
However, when I want to estimate the marginal effects with
Code:
margins, dydx(*) predict(outcome(3))
It just gives me the error message "could not calculate numerical derivatives -- discontinuous region with missing values encountered
r(459);"

I can avoid this problem by dropping the binary trust variable but this is not what I want. Also when running the binary logit, it works perfectly with the specified variables. Can someone explain why the calculation does not run through? Is there a way to fix this or are the variables simply not made for this model?

Thanks!!!