Hello everybody,

I have a question regarding regression's (statistical) interpretation with an interaction term. I find it difficult to see what can ultimately be said from the regression. I want to measure the patent output (Patents_w) of venture capital activities (VC_lag1) under the moderating influence of a founder CEO (dummy variable; 1=Founder CEO, 0=not a founder). For this purpose, I have created the following negative binomial regression.

Code:
xtnbreg Patents_w VC_lag1 FounderCEO c.VC_lag1#1.FounderCEO  firm_size_loglag1 xrdat_wlag1 industry_q_wlag1 os_w_lag1 i.fyear i.GSECTOR, fe

From this, I get the following results. Now I know that the interpretation is not easy with interaction terms, and one probably uses the margins command. My question is
a) it is possible to make a general statement regarding the interaction term? Because FounderCEO#c.VC_lag1 is highly non-significant?
b) what is the basic interpretation of margins command?
c) So the interpretation would be that if Founder CEO=0, then the marginal effect would be that innovation output increases by the value .0065886, whereas with FounderCEO no statement is possible because there is no significance and the confidence interval shows no concrete swing in one direction?

Code:
        Patents_w | Coefficient  Std. err.      z    P>|z|     [90% conf. interval]
----------------------------+----------------------------------------------------------------
         VC_lag1  |   .0065886   .0032773     2.01   0.044     .0011978    .0119793
       FounderCEO |   .8644112   .1764986     4.90   0.000     .5740968    1.154726
FounderCEO#c.VC_lag1 |
               1  |  -.0003116   .0070244    -0.04   0.965    -.0118657    .0112425
Margins Output
Code:
 margins FounderCEO, dydx(VC_lag1) level(90)
Code:
Expression: Linear prediction, predict()
dy/dx wrt:  VC_lag1

--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   std. err.      z    P>|z|     [90% conf. interval]
---------------+----------------------------------------------------------------
       VC_lag1 |
    FounderCEO |
            0  |   .0065886   .0032773     2.01   0.044     .0011978    .0119793
            1  |    .006277   .0065364     0.96   0.337    -.0044745    .0170284
--------------------------------------------------------------------------------
Thanks a lot for the help!
Max