Dear all, I run into an apparently (at least, for me) strange result.
I have a longitudinal (even if not properly so, since my individuals are followed only for two waves) dataset, and I want to run a xtlogit, re model since my dependent variable is binary, and I am interested in estimating the effect of an interaction between gender (Xf) and my main explanatory variable (dummy) on my dependent.

Below an example of my data:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float dip long Xa double Xb long Xc float Xd long Xf
0 2 49 2 1 1
0 2 49 2 1 1
0 3 60 1 2 2
0 3 60 1 2 2
0 3 33 1 1 2
0 3 34 1 2 2
0 3 25 1 2 1
0 3 25 1 2 1
0 3 29 5 2 1
0 3 28 5 2 1
1 2 58 1 1 2
0 3 58 1 1 2
1 3 50 1 2 1
1 3 50 1 2 1
0 3 42 1 2 2
0 3 43 1 2 2
1 3 51 2 1 2
. . 51 2 . 2
1 3 53 1 2 2
1 3 53 1 2 2
end
label values Xa F004
label def F004 2 "Secondary", modify
label def F004 3 "Tertiary", modify
label values Xc emp_stat
label def emp_stat 1 "Employee", modify
label def emp_stat 2 "Self-employed", modify
label def emp_stat 5 "Other", modify
label values Xd partner_present
label def partner_present 1 "Yes", modify
label def partner_present 2 "No", modify
label values Xf B002
label def B002 1 "Male", modify
label def B002 2 "Female", modify
Then, I run the following model:
Code:
 xtlogit dip i.dummy##i.Xf i.Xa i.country i.wave Xb  i.Xc i.Xd, re
But when asking for the margins of my two variables of interest (Xf and dummy) as following, I obtain predicted probabilities which are out of the boundary 0-1. Am I doing something wrong?
Code:
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dummy |
          0  |   .1257196   .0282364     4.45   0.000     .0703773    .1810619
          1  |  -2.235678      .0522   -42.83   0.000    -2.337988   -2.133368
             |
          Xf |
       Male  |  -1.008945   .0496997   -20.30   0.000    -1.106354   -.9115351
     Female  |  -.4854246   .0306359   -15.84   0.000    -.5454698   -.4253793
             |
    dummy#Xf |
     0#Male  |   -.241434   .0532371    -4.54   0.000    -.3457769   -.1370912
   0#Female  |   .2807271   .0334563     8.39   0.000      .215154    .3463001
     1#Male  |  -2.605775   .0931325   -27.98   0.000    -2.788312   -2.423239
   1#Female  |  -2.079428   .0589252   -35.29   0.000    -2.194919   -1.963936
Thanks a lot im advance. Best, G.