Hi everyone,

I’m new to Stata (using version 15.1 on Windows 10) and regard my statistical knowledge as rather basic.
I investigate how firms can take advantage of their investors' assets.
My data is organized as an unbalanced panel with firm ID as panel variable and report period as time variable. There are 149 firms and 490 observations.

For my first and second hypotheses my dependent variable is a 0.5-paced score ranging from 0 to 10. My independent variable is a dummy.

H1:
Dependent variable: revenues, rev_rating; 0.5-paced score from 0-10
Independent variable: theoretical fit, theo_fit; dummy, 1 if there is a potential fit between two specific entities, 0 otherwise
Controls: batch (categorical, 7 options), age (number of months, 23-100), year (controlling for the year in which rating has been performed; categorical, 3 options), status (categorical, 4 options), no_head (size in number of employees, 0-1,100), office (categorical, 12 options)

H2:
Dependent variable: rev_rating; 0.5-paced score from 0-10
Independent variable: cur_fit; dummy; 1 if there is an actual fit between the startup and another entity, 0 otherwise

Note: theo_fit=1 (independent variable in H1 and upcoming H3) is a prerequisite for cur_fit=1., but not all theo_fit=1 also have cur_fit=1

I got recommended to run xtologit and compare it with xtoprobit. However, I’m not sure whether this is the right thing to do since my dependent variable is a score rather than a dummy. I cannot run any fixed effects regressions because my independent variable doesn’t change over time and gets omitted. Does someone have other recommendations?

As an example:

Code:
xtologit rev_rating i.theo_fit i.batch months_agreement i.year no_head i.office
Code:
Fitting comparison model:

Iteration 0:   log likelihood = -1198.6864  
Iteration 1:   log likelihood = -1129.2539  
Iteration 2:   log likelihood = -1124.4294  
Iteration 3:   log likelihood = -1123.7448  
Iteration 4:   log likelihood = -1123.7439  
Iteration 5:   log likelihood = -1123.7439  

Refining starting values:

Grid node 0:   log likelihood = -1083.4717

Fitting full model:

Iteration 0:   log likelihood = -1083.4717  
Iteration 1:   log likelihood = -1043.0614  
Iteration 2:   log likelihood = -1037.7987  
Iteration 3:   log likelihood = -1037.1197  
Iteration 4:   log likelihood = -1037.1119  
Iteration 5:   log likelihood = -1037.1119  

Random-effects ordered logistic regression      Number of obs     =        486
Group variable: id_venture                      Number of groups  =        147

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          2
                                                              avg =        3.3
                                                              max =          6

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(22)     =      64.83
Log likelihood  = -1037.1119                    Prob > chi2       =     0.0000

----------------------------------------------------------------------------------
      rev_rating |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
                 |
      1.theo_fit |   1.667928   .5067086     3.29   0.001      .674797    2.661058
                 |
           batch |
              2  |  -.9373067   1.107814    -0.85   0.398    -3.108582    1.233968
              3  |  -2.892619   1.705272    -1.70   0.090    -6.234891    .4496523
              4  |  -3.495811   2.358969    -1.48   0.138    -8.119304    1.127683
              5  |  -6.004796   3.021248    -1.99   0.047    -11.92633   -.0832591
              6  |  -7.476233   3.183285    -2.35   0.019    -13.71536   -1.237108
              7  |  -9.458639   3.571192    -2.65   0.008    -16.45805    -2.45923
                 |
months_agreement |  -.1657581   .0721907    -2.30   0.022    -.3072492    -.024267
                 |
            year |
           2016  |   .1925695   .2060094     0.93   0.350    -.2112014    .5963404
           2017  |   .6511408   .3802286     1.71   0.087    -.0940935    1.396375
                 |
         no_head |   .0048509   .0025276     1.92   0.055    -.0001031     .009805
                 |
          office |
              2  |   2.032175   2.267954     0.90   0.370    -2.412932    6.477282
              3  |   4.102537    1.47625     2.78   0.005      1.20914    6.995935
              4  |   2.847437   1.613429     1.76   0.078    -.3148246    6.009699
              5  |  -1.262037    2.06315    -0.61   0.541    -5.305735    2.781662
              6  |    2.58405    1.24929     2.07   0.039      .135487    5.032614
              7  |   3.941555   1.775421     2.22   0.026     .4617931    7.421316
              8  |   4.322308   1.225161     3.53   0.000     1.921036     6.72358
              9  |    4.19991   1.351371     3.11   0.002     1.551273    6.848548
             10  |   1.167777   1.378502     0.85   0.397    -1.534037    3.869591
             11  |   3.188848   1.320014     2.42   0.016     .6016677    5.776028
             12  |    3.05598   1.404414     2.18   0.030     .3033797    5.808581
-----------------+----------------------------------------------------------------
           /cut1 |   -13.3631   6.155648                     -25.42795   -1.298253
           /cut2 |  -12.31746   6.151867                      -24.3749   -.2600186
           /cut3 |  -12.23387   6.151623                     -24.29083   -.1769115
           /cut4 |  -11.42768   6.149416                     -23.48032    .6249499
           /cut5 |  -10.57142   6.146626                     -22.61858    1.475747
           /cut6 |  -10.03098   6.144623                     -22.07422     2.01226
           /cut7 |   -9.96498   6.144372                     -22.00773    2.077769
           /cut8 |  -9.433196   6.142337                     -21.47195    2.605563
           /cut9 |  -9.382367   6.142146                     -21.42075    2.656017
          /cut10 |  -8.348679   6.137557                     -20.37807    3.680712
          /cut11 |   -7.58178   6.134757                     -19.60568    4.442124
          /cut12 |  -7.193055   6.134072                     -19.21561    4.829505
          /cut13 |  -6.611126   6.133662                     -18.63288     5.41063
          /cut14 |   -5.94365   6.133377                     -17.96485    6.077548
          /cut15 |  -5.750003   6.133178                     -17.77081    6.270806
          /cut16 |  -4.813253   6.131405                     -16.83059     7.20408
-----------------+----------------------------------------------------------------
       /sigma2_u |   5.965185   1.171412                      4.059471    8.765534
----------------------------------------------------------------------------------
LR test vs. ologit model: chibar2(01) = 173.26        Prob >= chibar2 = 0.0000
Moreover, I got the feeling that my third hypothesis cannot be tested by xtlogit nor xtprobit. Here, both, my dependent (cur_fit) and my independent (theo_fit) variables are dummies moderated by another dummy variable (share_info). 302 observations are not considered (Is that because the independent variable theo_fit doesn't change over time?).

H3:
Dependent variable: cur_fit
Independent variable: theo_fit
Moderating variable: information sharing, share_info

I ran

Code:
xtlogit cur_fit i.theo_fit##i.share_info
and get

Code:
note: 0.theo_fit != 0 predicts failure perfectly
      0.theo_fit dropped and 302 obs not used

note: 1.theo_fit omitted because of collinearity
note: 1.theo_fit#1.share_info omitted because of collinearity

Fitting comparison model:

Iteration 0:   log likelihood = -104.60614  
Iteration 1:   log likelihood = -104.38846  
Iteration 2:   log likelihood = -104.38808  
Iteration 3:   log likelihood = -104.38808  

Fitting full model:

tau =  0.0     log likelihood = -104.38808
tau =  0.1     log likelihood = -99.379343
tau =  0.2     log likelihood = -94.396503
tau =  0.3     log likelihood = -89.452781
tau =  0.4     log likelihood = -84.509089
tau =  0.5     log likelihood = -79.488327
tau =  0.6     log likelihood = -74.273074
tau =  0.7     log likelihood = -68.676386
tau =  0.8     log likelihood = -62.379474

Iteration 0:   log likelihood = -68.679948  
Iteration 1:   log likelihood = -35.388335  (not concave)
Iteration 2:   log likelihood = -35.206976  (not concave)
Iteration 3:   log likelihood = -35.071197  (not concave)
Iteration 4:   log likelihood = -35.014527  (not concave)
Iteration 5:   log likelihood =  -34.90272  (not concave)
Iteration 6:   log likelihood = -34.746671  (not concave)
Iteration 7:   log likelihood = -34.608508  (not concave)
Iteration 8:   log likelihood = -34.498735  (not concave)
Iteration 9:   log likelihood = -34.498735  (not concave)
Iteration 10:  log likelihood = -34.417236  (not concave)
Iteration 11:  log likelihood = -27.688751  (not concave)
Iteration 12:  log likelihood = -25.901447  
Iteration 13:  log likelihood = -25.853734  (not concave)
Iteration 14:  log likelihood = -24.131304  (not concave)
Iteration 15:  log likelihood = -20.304816  
Iteration 16:  log likelihood = -19.016582  
Iteration 17:  log likelihood = -15.744041  
Iteration 18:  log likelihood = -15.722299  
Iteration 19:  log likelihood = -15.722287  
Iteration 20:  log likelihood = -15.722287  

Random-effects logistic regression              Number of obs     =        188
Group variable: id_venture                      Number of groups  =         55

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =          2
                                                              avg =        3.4
                                                              max =          6

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(1)      =       0.67
Log likelihood  = -15.722287                    Prob > chi2       =     0.4144

-------------------------------------------------------------------------------------
            cur_fit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
           theo_fit |
                 0  |          0  (empty)
                 1  |          0  (omitted)
                    |
       1.share_info |   .7869919   .9642079     0.82   0.414    -1.102821    2.676805
                    |
theo_fit#share_info |
               0 0  |          0  (empty)
               0 1  |          0  (empty)
               1 1  |          0  (omitted)
                    |
              _cons |  -.4224731    .726772    -0.58   0.561     -1.84692    1.001974
--------------------+----------------------------------------------------------------
           /lnsig2u |   3.475415   .1946816                      3.093846    3.856984
--------------------+----------------------------------------------------------------
            sigma_u |   5.684297    .553314                      4.696996    6.879128
                rho |   .9075908   .0163279                      .8702305    .9349986
-------------------------------------------------------------------------------------
LR test of rho=0: chibar2(01) = 177.33                 Prob >= chibar2 = 0.000
In the end, I made a contingency table to check the frequencies of every possible outcome. Is there another way to analyze it?

Hope I posted it the right way. If you lack any information let me know.

Thank you very, very much in advance!