Hi everyone,

I am estimating a moderating effect with a random effects regression model and it gives me the results indicated below. As the interaction term shows a significant relationship, I would like to graphically show how this moderating effect evolves on ROE. However, I can only find how to create a margins plot from the two interacting variables, but not the impact on the dependent variable (ROE in this case).

Hope someone can help!

Code:
xtreg ROE_w c.laggedGSCMP##i.laggedEMS Firmrisk_w Firmsize_w i.Industry i.year, re

Random-effects GLS regression                   Number of obs      =      3704
Group variable: ID                              Number of groups   =       463

R-sq:  within  = 0.0154                         Obs per group: min =         8
       between = 0.0897                                        avg =       8.0
       overall = 0.0301                                        max =         8

                                                Wald chi2(17)      =     95.26
corr(u_i, X)   = 0 (assumed)                    Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------------
                  ROE_w |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
            laggedGSCMP |  -.0513154   .0717939    -0.71   0.475    -.1920287     .089398
            1.laggedEMS |  -.0228706   .0314698    -0.73   0.467    -.0845502    .0388091
                        |
laggedEMS#c.laggedGSCMP |
                     1  |   .2009424   .0930021     2.16   0.031     .0186617    .3832232
                        |
             Firmrisk_w |  -.0095493   .0619435    -0.15   0.877    -.1309563    .1118577
             Firmsize_w |   .0329511   .0083621     3.94   0.000     .0165616    .0493406
                        |
               Industry |
                     2  |  -.0058314   .0503369    -0.12   0.908      -.10449    .0928272
                     3  |  -.0471294   .0430901    -1.09   0.274    -.1315843    .0373256
                     4  |  -.0536516   .0583197    -0.92   0.358    -.1679561    .0606528
                     5  |  -.1049833   .0432338    -2.43   0.015    -.1897201   -.0202466
                     6  |  -.0863122   .0387198    -2.23   0.026    -.1622017   -.0104227
                        |
                   year |
                  2008  |  -.0465011   .0318643    -1.46   0.144    -.1089541    .0159518
                  2009  |   -.138029   .0318752    -4.33   0.000    -.2005032   -.0755548
                  2010  |  -.0493769   .0318645    -1.55   0.121    -.1118301    .0130763
                  2011  |   -.005929   .0320594    -0.18   0.853    -.0687642    .0569062
                  2012  |  -.0636718   .0320582    -1.99   0.047    -.1265047    -.000839
                  2013  |   .0183746   .0319767     0.57   0.566    -.0442986    .0810477
                  2014  |   .0199688   .0323688     0.62   0.537    -.0434728    .0834105
                        |
                  _cons |  -.0430078   .0740766    -0.58   0.562    -.1881953    .1021797
------------------------+----------------------------------------------------------------
                sigma_u |  .13493434
                sigma_e |  .48411832
                    rho |  .07208581   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------