xtmlogit pwcompare and contrast postestimation error Stata17

[Madu Abuchi]

panel multinomial logit model was introduced in Stata 17, however, I'm having issues using the pwcompare and contrast postestimation commands after fitting an xtmlogit model. Can someone please suggest why I'm having this error or is it that what I'm attempting is not practically possible or being done in the wrong way.

webuse estatus, clear
xtset id
xtmlogit estatus i.hhchild age hhincome i.hhsigno i.bwinner
contrast hhchild

I get the r(3200) error message - something to do with the matrix:

 *:  3200  conformability error
             _contrast():     -  function returned error
                 <istmt>:     -  function returned error

[Alejandra Suarez]

I am running into the exact same problem after running xtmlogit and getting my results. Pwcompare and Contrast appear in the postestimation list for xtmlogit, however, the same error always appears. I am working with Stata/SE 17.0 for Mac.

Were you able to find a solution to the error?

Thank you.

Alejandra

[Joerg Luedicke (StataCorp)]

Hi Alejandra and Madu,

Thank you for bringing this to our attention. This is a bug that we will fix in a future update. We apologize for the inconvenience.

In the meantime, the bug is related to the estimability check contrast is performing. Thus, if you know that your contrasts are well defined, you can get around this problem by bypassing that check using the noestimcheck option. Here is an example:

. webuse estatus, clear
(Fictional employment status data)

. xtset id

Panel variable: id (unbalanced)

. xtmlogit estatus i.hhchild age hhincome i.hhsigno i.bwinner

Fitting comparison model ...

Refining starting values:

Grid node 0:   log likelihood = -4483.1721
Grid node 1:   log likelihood = -4516.6753

Fitting full model:

Iteration 0:   log likelihood = -4483.1721  
Iteration 1:   log likelihood = -4474.3849  
Iteration 2:   log likelihood = -4468.9353  
Iteration 3:   log likelihood = -4468.8415  
Iteration 4:   log likelihood = -4468.8413  

Random-effects multinomial logistic regression       Number of obs    =  4,761
Group variable: id                                   Number of groups =    800

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      5
                                                                  avg =    6.0
                                                                  max =      7

Integration method: mvaghermite                      Integration pts. =      7

                                                     Wald chi2(10)    = 239.26
Log likelihood = -4468.8413                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------------
           estatus | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
Out_of_labor_force |
           hhchild |
              Yes  |   .4628125   .0962758     4.81   0.000     .2741154    .6515096
               age |   -.004825   .0066428    -0.73   0.468    -.0178446    .0081946
          hhincome |  -.0046922    .001839    -2.55   0.011    -.0082965   -.0010879
                   |
           hhsigno |
              Yes  |   .4967056   .0946442     5.25   0.000     .3112063    .6822049
                   |
           bwinner |
              Yes  |  -.4740919   .0727992    -6.51   0.000    -.6167756   -.3314082
             _cons |  -.4787579   .2845139    -1.68   0.092    -1.036395    .0788792
-------------------+----------------------------------------------------------------
Unemployed         |
           hhchild |
              Yes  |  -.0401989    .119596    -0.34   0.737    -.2746027    .1942049
               age |   .0042644   .0081818     0.52   0.602    -.0117716    .0203004
          hhincome |  -.0308468   .0026529   -11.63   0.000    -.0360463   -.0256473
                   |
           hhsigno |
              Yes  |      .0968   .1192659     0.81   0.417    -.1369568    .3305568
                   |
           bwinner |
              Yes  |  -.2252587   .0951984    -2.37   0.018    -.4118441   -.0386733
             _cons |  -.0953821   .3508736    -0.27   0.786    -.7830817    .5923175
-------------------+----------------------------------------------------------------
Employed           |  (base outcome)
-------------------+----------------------------------------------------------------
            var(u1)|   .8587807   .1090216                      .6696113    1.101392
            var(u2)|   .7370366   .1388917                      .5094287    1.066338
------------------------------------------------------------------------------------
LR test vs. multinomial logit: chi2(2) = 225.31           Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. contrast hhchild, noestimcheck

Contrasts of marginal linear predictions

Margins: asbalanced

------------------------------------------------
             |         df        chi2     P>chi2
-------------+----------------------------------
Out_of_lab~e |
     hhchild |          1       23.11     0.0000
------------------------------------------------

. contrast hhchild, equation(Unemployed) noestimcheck

Contrasts of marginal linear predictions

Margins: asbalanced

------------------------------------------------
             |         df        chi2     P>chi2
-------------+----------------------------------
Unemployed   |
     hhchild |          1        0.11     0.7368
------------------------------------------------

I hope this helps.

Best,
Joerg