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

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.

I got recommended to run

As an example:

Moreover, I got the feeling that my third hypothesis cannot be tested by

H3:

I ran

and get

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!

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=1I 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

**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_infoI ran

Code:

xtlogit cur_fit i.theo_fit##i.share_info

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

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

Thank you very, very much in advance!