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  • #16
    Hi Jeff,

    Thanks for the clarification on logs. Regarding the interaction, part of my study is to determine whether the relationship between ESG and the cost of capital matters more for companies domiciled in countries with stronger or weaker legal environments, hence the interaction. I plan on conducting a separate regression with just ESG.

    For the xtreg, my results are insignificant. I have explored multicollinearity and dropped some variables. I am new to Stata and have done extensive searching, but I cant figure out the issue - I am using the same variables as literature on the topic which consistently reports significance ( I am using a different dataset although).

    Code:
     
    
    xtreg WACC i.LEGAL#c.ESG TQ ASG CUR LEV ROA BT INFL ln_SIZE i.Q_DATE, fe vce(cluster ID)
    
    Fixed-effects (within) regression               Number of obs     =      1,019
    Group variable: ID                              Number of groups  =         51
    
    R-squared:                                      Obs per group:
         Within  = 0.2217                                         min =         19
         Between = 0.0581                                         avg =       20.0
         Overall = 0.1204                                         max =         20
    
                                                    F(29, 50)         =      17.02
    corr(u_i, Xb) = -0.4157                         Prob > F          =     0.0000
    
                                        (Std. err. adjusted for 51 clusters in ID)
    ------------------------------------------------------------------------------
                 |               Robust
            WACC | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
    -------------+----------------------------------------------------------------
     LEGAL#c.ESG |
              0  |    .117814   .0772885     1.52   0.134    -.0374245    .2730525
              1  |   .0234984   .0592518     0.40   0.693    -.0955122    .1425091
                 |
              TQ |   .1236057   .1094523     1.13   0.264    -.0962357    .3434471
             ASG |  -.0031606   .0021316    -1.48   0.144    -.0074421    .0011208
             CUR |  -.6016558   .6505541    -0.92   0.359    -1.908332    .7050205
             LEV |  -.2120949   .1508573    -1.41   0.166    -.5151007    .0909108
             ROA |  -.0882799   .0925487    -0.95   0.345    -.2741694    .0976096
              BT |   1.324192   .7911091     1.67   0.100    -.2647978    2.913181
            INFL |  -.0378087   .0685163    -0.55   0.584    -.1754277    .0998103
         ln_SIZE |  -.3366782   1.212594    -0.28   0.782    -2.772245    2.098888
                 |
          Q_DATE |
            233  |  -.2643647   .6454358    -0.41   0.684    -1.560761    1.032031
            234  |  -.3462174   .3578227    -0.97   0.338    -1.064925    .3724906
            235  |  -.0527402   .6747097    -0.08   0.938    -1.407934    1.302454
            236  |  -1.263111   .3236721    -3.90   0.000    -1.913226   -.6129967
            237  |  -.9502819     .41123    -2.31   0.025    -1.776262   -.1243022
            238  |  -.7070823   .4532081    -1.56   0.125    -1.617378    .2032129
            239  |  -.5344184   .4771554    -1.12   0.268    -1.492813    .4239764
            240  |  -1.258724   .5301932    -2.37   0.021    -2.323648   -.1937994
            241  |   1.133952   1.014136     1.12   0.269    -.9029992    3.170903
            242  |   1.815448    1.08304     1.68   0.100    -.3599018    3.990797
            243  |  -.4841026   .7798078    -0.62   0.538    -2.050393    1.082187
            244  |   1.815126   1.015715     1.79   0.080    -.2249989     3.85525
            245  |   4.859438   1.413294     3.44   0.001     2.020754    7.698121
            246  |   2.378434   1.157898     2.05   0.045     .0527283     4.70414
            247  |   2.454008   1.238441     1.98   0.053    -.0334741     4.94149
            248  |   2.010818   1.379193     1.46   0.151    -.7593731    4.781009
            249  |   2.960136   2.111618     1.40   0.167    -1.281173    7.201445
            250  |   2.882623   2.172732     1.33   0.191    -1.481438    7.246683
            251  |    3.31179   2.481841     1.33   0.188    -1.673133    8.296714
                 |
           _cons |    14.9167   12.35549     1.21   0.233    -9.900025    39.73342
    -------------+----------------------------------------------------------------
         sigma_u |  3.7941641
         sigma_e |  4.2754813
             rho |  .44056605   (fraction of variance due to u_i)
    ------------------------------------------------------------------------------
    
    . margins LEGAL,dydx(ESG)
    
    Average marginal effects                                 Number of obs = 1,019
    Model VCE: Robust
    
    Expression: Linear prediction, predict()
    dy/dx wrt:  ESG
    
    ------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
    -------------+----------------------------------------------------------------
    ESG          |
           LEGAL |
              0  |    .117814   .0772885     1.52   0.127    -.0336686    .2692966
              1  |   .0234984   .0592518     0.40   0.692    -.0926329    .1396298
    ------------------------------------------------------------------------------

    Comment


    • #17

      Code:
      reghdfe WACC LEGAL ESG c.LEGAL#c.ESG TQ ASG CUR LEV ROA BT INFL ln_SIZE, absorb(ID Q_DATE) cluster(ID)
      Q_DATE should be a time variable, not just quarterly dummies.

      Comment


      • #18
        George: from the output in #16, it is a time variable running from 232 to 251.

        Ethan: Even if you’re interested in interactive effects, my must include the levels. It doesn’t make much sense otherwise.

        Comment

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