FEM categorial variable output

[Hannah Zllnr]

Hello,

In my fixed effect model with the effect of parenting styles (categorial) on problemativ behaviour (metric), not every specification of the categorial variable is shown.
Parenting styles have 4 categories, but the output of my FEM only shows 3 of them and I have no idea why this is the case.
Do you have any idea why I'm only seeing 3 out of 4 categories of the variable parenting styles?

Regression output:
(Std. Err. adjusted for 77 clusters in id)
-------------------------------------------------------------------------------
| Robust
conduct_paco | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------+----------------------------------------------------------------
erziehungss~e |
Autoritär | .0460064 .0334382 1.38 0.173 -.0205916 .1126044
Vernachläs~d | .0882347 .0440646 2.00 0.049 .0004724 .1759971
Verwöhnend | -.0048964 .0308254 -0.16 0.874 -.0662905 .0564977
|
_cons | .2840729 .0193521 14.68 0.000 .2455298 .322616
--------------+----------------------------------------------------------------
sigma_u | .18830684
sigma_e | .19903259
rho | .47233038 (fraction of variance due to u_i)
-------------------------------------------------------------------------------


Command:
xtset cid wave
xtreg conduct_paco i.erziehungsstile, fe vce(cluster id)

Here is my data used for the command above

* Example generated by -dataex-. To install: ssc install dataex
clear
input long(id cid) int wave float(erziehungsstile conduct_paco)
  111000   111203  2 1  .2
  111000   111203  3 4  .8
  111000   111203  4 3 1.2
  111000   111203  5 4   1
  111000   111203  6 4  .8
  111000   111203  7 1  .5
  111000   111203  8 4  .2
  111000   111203  9 3   0
 4858000  4858201  4 1   0
 4858000  4858201  5 1  .2
 4858000  4858201  6 2  .4
 4858000  4858201  7 1 1.2
 4858000  4858201  8 1  .8
 4858000  4858201  9 4  .8
 4858000  4858201 10 1  .6
 4858000  4858201 11 1  .6
 8948000  8948201  2 1   0
 8948000  8948201  3 1  .2
 8948000  8948201  4 1  .2
 8948000  8948201  5 1  .2
 8948000  8948201  6 1  .2
 8948000  8948201  7 4  .4
 8948000  8948201  8 4  .2
 8948000  8948201  9 2  .8
11295000 11295201  4 1  .2
11295000 11295201  5 4  .2
11295000 11295201  6 1  .6
11295000 11295201  7 1  .2
11295000 11295201  8 1 .25
11295000 11295201  9 4  .2
11295000 11295201 10 4   0
11295000 11295201 11 1   0
19673000 19673201  2 3   0
19673000 19673201  3 4  .2
19673000 19673201  4 4  .2
19673000 19673201  5 3  .2
19673000 19673201  6 4  .2
19673000 19673201  7 3  .2
19673000 19673201  8 4  .2
19673000 19673201  9 3   0
35026000 35026203  3 3  .8
35026000 35026203  4 3  .6
35026000 35026203  5 3  .6
35026000 35026203  6 3  .4
35026000 35026203  7 3  .6
35026000 35026203  8 3  .6
35026000 35026203  9 3  .2
35026000 35026203 10 3  .4
45091000 45091202  2 1  .4
45091000 45091202  3 1  .8
45091000 45091202  4 1   0
45091000 45091202  5 1   0
45091000 45091202  6 1   0
45091000 45091202  7 1  .4
45091000 45091202  8 1  .4
45091000 45091202  9 4   0
55124000 55124201  4 1  .2
55124000 55124201  5 1  .6
55124000 55124201  6 1  .2
55124000 55124201  7 1  .2
55124000 55124201  8 1  .4
55124000 55124201  9 2  .6
55124000 55124201 10 2  .6
55124000 55124201 11 3   1
61002000 61002202  3 2  .4
61002000 61002202  4 1  .4
61002000 61002202  5 1  .2
61002000 61002202  6 3  .4
61002000 61002202  7 2  .2
61002000 61002202  8 2  .2
61002000 61002202  9 3  .2
61002000 61002202 10 3  .2
61002000 61002203  3 2  .4
61002000 61002203  4 3  .2
61002000 61002203  5 1  .2
61002000 61002203  6 3  .4
61002000 61002203  7 2  .2
61002000 61002203  8 2  .2
61002000 61002203  9 3  .2
61002000 61002203 10 3  .2
73923000 73923202  2 1   0
73923000 73923202  3 1   0
73923000 73923202  4 1   0
73923000 73923202  5 1   0
73923000 73923202  6 1  .2
73923000 73923202  7 1  .2
73923000 73923202  8 1   0
73923000 73923202  9 3  .4
80857000 80857202  3 1   0
80857000 80857202  4 1  .2
80857000 80857202  5 1  .2
80857000 80857202  6 1   0
80857000 80857202  7 1  .2
80857000 80857202  8 1   1
80857000 80857202  9 4  .8
80857000 80857202 10 1  .6
86889000 86889201  2 1  .2
86889000 86889201  3 1   0
86889000 86889201  4 1  .6
86889000 86889201  5 1  .2
end
label values wave WAVE_prt2
label def WAVE_prt2 2 "2 2009/10", modify
label def WAVE_prt2 3 "3 2010/11", modify
label def WAVE_prt2 4 "4 2011/12", modify
label def WAVE_prt2 5 "5 2012/13", modify
label def WAVE_prt2 6 "6 2013/14", modify
label def WAVE_prt2 7 "7 2014/15", modify
label def WAVE_prt2 8 "8 2015/16", modify
label def WAVE_prt2 9 "9 2016/17", modify
label def WAVE_prt2 10 "10 2017/18", modify
label def WAVE_prt2 11 "11 2018/19", modify
label values erziehungsstile erziehungsstile
label def erziehungsstile 1 "Autoritativ", modify
label def erziehungsstile 2 "Autoritär", modify
label def erziehungsstile 3 "Vernachlässigend", modify
label def erziehungsstile 4 "Verwöhnend", modify
label values conduct_paco conduct_paco
label def conduct_paco 0 "0 niedrig", modify

[Noah Mkasanga]

Hannah,
The smallest value of the categorical variable is missing because by default, Stata sets the smallest value as the base when factor variable notation is used in the equation.
For more examples, please type help factor variable.

[Hannah Zllnr]

Thank you Noah, the option ibn.erziehungsstile" helped with it, since ibn. means no base levels. However, another category is now omitted after using this command.
Is there any solution for this case?

[Rich Goldstein]

you cannot show all categories if you include a constant; the mle and pa versions of -xtreg- allow the noconstant option so, if either of these work for you, you could use those

[Arief RRH]

Hello Hannah, may I know if you used the robust estimation for your model?