Dear statalisters,

I am studying why people not use contraception in an unbalanced panel of 11 waves. As I am interested in comparing within and between effects I ran a within-between or hybrid model. I calculated the within- and between components for all time-varying variables "by hand" and ran xtlogit, re. Then I used the xthybrid command by Schunk and Perales to see if they produce the same results . However, I find that coefficients are not exactly the same.
I did several checks. I am sure I use exactly the same sample, I compared the within-and between components I created with those created by the xthybrid (using the keepvars-option), and I adjusted the integrationpoints to in the xtlogit command to 7 so they are comparable between models. Both commands use pseudomaximumlikelihood and the same integration-method.
I hope somebody can help me to understand why that is the case. Does this have something to do with the fact that xthybrid is based on meglm command?

I use Stata/SE 16.0.

Syntax for creating within- and between components manually
Code:
foreach var of varlist  agegroup2_1 agegroup2_2 agegroup2_3 agegroup2_4  {
    bysort id (wave): egen m_`var'=mean(`var') if hsample1==1          // mean (within person)
    bysort id (wave): gen d_`var'=`var'-m_`var' if hsample1==1        // deviation of individual value from mean (within person)
}
Heres the commands and regression outputs. For ease of presentation I am showing a model with only few explanatory variables.

Code:
. cap drop B_*  W_*   

. xthybrid inconsistency3c_d1 /*agegroup2_1*/ agegroup2_2 agegroup2_3 agegroup2_4  /*breastfeed21*/ breastfeed22 breastfeed23 if hsample1==
> 1, vce(robust) clusterid(id) family(binomial) link(logit) full keepvars


-------------------------------------------------------------------------------------------------------------------------------------------
Model model
-------------------------------------------------------------------------------------------------------------------------------------------

Mixed-effects GLM                               Number of obs     =     18,084
Family:                binomial
Link:                     logit
Group variable:              id                 Number of groups  =      5,035

                                                Obs per group:
                                                              min =          1
                                                              avg =        3.6
                                                              max =         10

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(10)     =     544.81
Log pseudolikelihood = -6223.9484               Prob > chi2       =     0.0000
                                       (Std. Err. adjusted for 5,035 clusters in id)
------------------------------------------------------------------------------------
                   |               Robust
inconsistency3c_d1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
    W__agegroup2_2 |   1.154933   .2486763     4.64   0.000     .6675365     1.64233
    W__agegroup2_3 |   1.889027   .2985934     6.33   0.000     1.303795    2.474259
    W__agegroup2_4 |   2.295771   .3402473     6.75   0.000     1.628899    2.962644
   W__breastfeed22 |   3.257546   .2634088    12.37   0.000     2.741275    3.773818
   W__breastfeed23 |   1.344047   .2551749     5.27   0.000     .8439133     1.84418
    B__agegroup2_2 |   2.106907   .2580269     8.17   0.000     1.601184    2.612631
    B__agegroup2_3 |   3.521265   .2428555    14.50   0.000     3.045277    3.997253
    B__agegroup2_4 |    4.85516   .7315018     6.64   0.000     3.421443    6.288877
   B__breastfeed22 |   4.904525   .6305259     7.78   0.000     3.668717    6.140333
   B__breastfeed23 |   1.426821   .5580139     2.56   0.011     .3331338    2.520508
             _cons |  -6.170052   .2564959   -24.06   0.000    -6.672774   -5.667329
-------------------+----------------------------------------------------------------
id                 |
         var(_cons)|   13.37639   1.321677                      11.02135    16.23467
------------------------------------------------------------------------------------
Please remember to remove any variables beginning with the prefix B__, W__ or R__ from the data before executing xthybrid again

.
. xtlogit inconsistency3c_d1 /*d_agegroup2_1 d_breastfeed21*/ c.d_agegroup2_2 c.d_agegroup2_3 c.d_agegroup2_4 c.d_breastfeed22 c.d_breastfe
> ed23 ///
> c.m_agegroup2_2  c.m_agegroup2_3 c.m_agegroup2_4 /*m_agegroup2_1 m_breastfeed21 */ c.m_breastfeed22  c.m_breastfeed23  if hsample1==1, re
>  vce(robust) i(id) intpoints(7)

Fitting comparison model:

Iteration 0:   log pseudolikelihood = -8702.3206  
Iteration 1:   log pseudolikelihood = -8189.2047  
Iteration 2:   log pseudolikelihood =  -8139.761  
Iteration 3:   log pseudolikelihood = -8138.7956  
Iteration 4:   log pseudolikelihood = -8138.7937  
Iteration 5:   log pseudolikelihood = -8138.7937  

Fitting full model:

tau =  0.0     log pseudolikelihood = -8138.7937
tau =  0.1     log pseudolikelihood = -7873.8225
tau =  0.2     log pseudolikelihood = -7629.4166
tau =  0.3     log pseudolikelihood = -7405.2845
tau =  0.4     log pseudolikelihood = -7199.9321
tau =  0.5     log pseudolikelihood = -7012.9971
tau =  0.6     log pseudolikelihood = -6846.7976
tau =  0.7     log pseudolikelihood = -6707.9118
tau =  0.8     log pseudolikelihood = -6613.7677

Iteration 0:   log pseudolikelihood =  -6703.822  
Iteration 1:   log pseudolikelihood = -6309.7395  
Iteration 2:   log pseudolikelihood = -6252.9039  
Iteration 3:   log pseudolikelihood = -6225.7805  
Iteration 4:   log pseudolikelihood = -6225.5742  
Iteration 5:   log pseudolikelihood = -6225.5742  (backed up)
Iteration 6:   log pseudolikelihood = -6221.5383  
Iteration 7:   log pseudolikelihood = -6221.4572  
Iteration 8:   log pseudolikelihood = -6221.4571  

Calculating robust standard errors:

Random-effects logistic regression              Number of obs     =     18,084
Group variable: id                              Number of groups  =      5,035

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          1
                                                              avg =        3.6
                                                              max =         10

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(10)     =     579.59
Log pseudolikelihood  = -6221.4571              Prob > chi2       =     0.0000

                                       (Std. Err. adjusted for 5,035 clusters in id)
------------------------------------------------------------------------------------
                   |               Robust
inconsistency3c_d1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
     d_agegroup2_2 |   1.051203   .2299139     4.57   0.000     .6005804    1.501826
     d_agegroup2_3 |   1.753846   .2787489     6.29   0.000     1.207508    2.300184
     d_agegroup2_4 |    2.13603   .3182763     6.71   0.000      1.51222     2.75984
    d_breastfeed22 |   3.108342   .2433278    12.77   0.000     2.631428    3.585255
    d_breastfeed23 |   1.284743   .2434488     5.28   0.000     .8075924    1.761894
     m_agegroup2_2 |   1.959986    .249079     7.87   0.000       1.4718    2.448172
     m_agegroup2_3 |   3.296934    .234279    14.07   0.000     2.837756    3.756113
     m_agegroup2_4 |   4.511199   .7167888     6.29   0.000     3.106319    5.916079
    m_breastfeed22 |   4.366374   .5636822     7.75   0.000     3.261577    5.471171
    m_breastfeed23 |   1.330811    .544356     2.44   0.014     .2638933     2.39773
             _cons |  -5.667056   .2271291   -24.95   0.000    -6.112221   -5.221891
-------------------+----------------------------------------------------------------
          /lnsig2u |    2.28083   .0642219                      2.154957    2.406702
-------------------+----------------------------------------------------------------
           sigma_u |   3.128066   .1004451                      2.937264    3.331262
               rho |   .7483784   .0120935                      .7239439    .7713332
------------------------------------------------------------------------------------
Once I understand what is going on I would like to include interactions of all variables by gender. Whats the appropriate way to do that using the xthybrid command since factor-variable notation is not allowed? Should create the interactions manually and include them in the syntax.

Looking forward to your replies!
Jasmin