Stata command when depvar is a dummy variable

[Jihad El Yaagoubi]

I want to run a study on the impact of financial performance on corporate social responsibility (CSR), where CSR is a dummy variable

reg CSR ROA Risk Size Age ib2.Industry,vce(cluster Companyscode)

Is this correct ? because when I add the prefix i.CSR, stata gives me this

 reg i.CSR ROA Risk Size Age ib2.Industry,vce(cluster Companyscode)
depvar may not be a factor variable
r(198);

[Joseph Coveney]

Yes, you do not normally use the factor variable syntax for outcome (dependent) variables.

Depending upon how many categories are in CSR, and whether the baseline category is coded as zero, you might prefer something like

logit CSR ROA Risk Size Age ib2.Industry, vce(cluster Companyscode)

or

mlogit CSR ROA Risk Size Age ib2.Industry, vce(cluster Companyscode)

[Jihad El Yaagoubi]

CSR is a binary variable, it takes 1 when the company is CSR labelled and 0 otherwise

mlogit CSR ROA Risk Size Age ib2.Industry, vce(cluster Companyscode)

this code isn't for pooled ols model ?

[Jihad El Yaagoubi]

Is this model correct and reliable knowing that CSR is binary variable 0 or 1 ?

reg CSR ROA Risk Size Age ib2.Industry,vce(cluster Companyscode)

Linear regression                               Number of obs     =        167
                                                F(7, 27)          =       8.26
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0503
                                                Root MSE          =     .47551

                          (Std. Err. adjusted for 28 clusters in Companyscode)
------------------------------------------------------------------------------
             |               Robust
         CSR |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         ROA |   .1539658   .0517074     2.98   0.006     .0478709    .2600606
        Risk |  -.0684991   .0471436    -1.45   0.158    -.1652298    .0282316
        Size |  -.0448225   .1000813    -0.45   0.658    -.2501723    .1605273
         Age |   .0033307   .0039281     0.85   0.404    -.0047291    .0113906
             |
    Industry |
          1  |   .2006988   .2308277     0.87   0.392    -.2729205     .674318
          3  |   .0580331   .2672971     0.22   0.830    -.4904152    .6064814
          4  |  -.0056543   .2621828    -0.02   0.983     -.543609    .5323004
             |
       _cons |   .6148575    .875565     0.70   0.489    -1.181653    2.411368
------------------------------------------------------------------------------

. linktest

      Source |       SS           df       MS      Number of obs   =       167
-------------+----------------------------------   F(2, 164)       =      4.35
       Model |  1.90707296         2   .95353648   Prob > F        =    0.0144
    Residual |  35.9492145       164  .219202527   R-squared       =    0.0504
-------------+----------------------------------   Adj R-squared   =    0.0388
       Total |  37.8562874       166  .228049924   Root MSE        =    .46819

------------------------------------------------------------------------------
         CSR |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        _hat |   .8949609   1.019796     0.88   0.381    -1.118662    2.908584
      _hatsq |   .1279554   1.171525     0.11   0.913     -2.18526    2.441171
       _cons |   .0195873   .2176179     0.09   0.928    -.4101068    .4492815
------------------------------------------------------------------------------

. estat ovtest

Ramsey RESET test using powers of the fitted values of CSR
       Ho:  model has no omitted variables
                 F(3, 156) =      0.15
                  Prob > F =      0.9267

. estat vif

    Variable |       VIF       1/VIF  
-------------+----------------------
         ROA |      2.81    0.355467
        Risk |      2.80    0.357675
        Size |      2.16    0.463243
         Age |      1.52    0.656552
    Industry |
          1  |      2.04    0.491148
          3  |      3.90    0.256617
          4  |      2.38    0.419777
-------------+----------------------
    Mean VIF |      2.52

[Richard Williams]

You should read up on logistic regression. One source is

https://www3.nd.edu/~rwilliam/stats3/index.html

But there are no doubt hundreds if not thousands of discussions online.