Comparing coefficients while keeping difference between other coefficients constant in mixed model

Dear Statalist users,

I would like to compare whether the slope is statistically different between two groups while pretending that the intercept was exactly the same between the groups.

A bit about the model: I run a growth curve model with economic resources (continuous variables in Euro) as my outcome measure. My explanatory variables are time since divorce ("divduration" in years) and whether a respondent actually experienced a divorce ("treat" with 1=divorced, 0=continuously married). So the base model looks as follows:

Code:

mi estimate, dots post: mixed wealth  c.divduration##i.treat $control1  || id: divduration if psmatched2 ==1, variance mle

I am now adding another interaction with a remarriage indicator:

Code:

mi estimate, dots post: mixed wealth c.divduration##i.treat##i.remar $control1 || id: divduration if psmatched2 ==1, variance mle

And get the following output:

Code:

Imputations (5):
  ..... done

Multiple-imputation estimates                   Imputations       =          5
Mixed-effects ML regression                     Number of obs     =      9,760

Group variable: id                              Number of groups  =      5,006
                                                Obs per group:
                                                              min =          1
                                                              avg =        1.9
                                                              max =          4
                                                Average RVI       =     6.4175
                                                Largest FMI       =     0.9839
DF adjustment:   Large sample                   DF:     min       =       4.18
                                                        avg       =      64.83
                                                        max       =     397.52
Model F test:       Equal FMI                   F(   7,  134.7)   =      11.17
                                                Prob > F          =     0.0000

-------------------------------------------------------------------------------------------
                   wealth |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
              divduration |   2221.492   643.0812     3.45   0.003      874.101    3568.884
                          |
                    treat |
                 Treated  |  -39046.84   8871.472    -4.40   0.000    -56692.11   -21401.57
                          |
      treat#c.divduration |
                 Treated  |  -297.1171   1363.105    -0.22   0.828    -2976.913    2382.679
                          |
                  1.remar |    9413.24   16072.45     0.59   0.562    -23172.01     41998.5
                          |
      remar#c.divduration |
                       1  |   1394.398   2100.509     0.66   0.509    -2796.951    5585.746
                          |
              treat#remar |
               Control#1  |          0  (empty)
               Treated#1  |          0  (omitted)
                          |
treat#remar#c.divduration |
               Control#1  |          0  (empty)
               Treated#1  |          0  (omitted)
                          |
       1.flag_firstwealth |  -13878.67   4438.822    -3.13   0.003     -22757.7   -4999.634
         1.flag_impwealth |   14860.63   6932.212     2.14   0.067    -1366.448    31087.72
                    _cons |   81025.73   5520.197    14.68   0.000     69689.74    92361.72
-------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Independent              |
             sd(divduration) |   11011.74   1056.377      8637.166    14039.15
                   sd(_cons) |    99006.4   6453.131      84522.53    115972.3
-----------------------------+------------------------------------------------
                sd(Residual) |   133315.1   9924.388      108806.7      163344
------------------------------------------------------------------------------

My problem/my question: I would now like to test whether there is a statistical difference between the growth curve of divorcees that are ever remarried compared to divorcees that are never-remarried while keeping their initial differences constant. So at the moment, remarried divorcees have 1394 Euros more right at divorce than never-married divorcees. I would like to pretend that there was no initial difference and then test whether the two differ in their growth rate after divorce.

Note: continuously married respondents can never be remarried and thus their interaction coefficient falls out of the model.

Data: SOEP
STATA: 16.1

I am not very experienced with postestimation commands and appreciate any advice on how I can solve this.

Thank you,
​​​​​​​Nicole