Dear Stata Members
Based on the below result, I have the following questions

Code:
reghdfe lever_w ///
> i.ibc##i.treat2 ///
> size_w nfa_ta_w cash_ta_w trade_credit_w sales_grow_w roa_w pb_w cfo_ta_w rddcc_dum div_dum nw_ta_w age , absorb
> (ff48##i.year code) cluster (code)
(dropped 43 singleton observations)
note: 1bn.ibc is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0
> e-09)
note: 1bn.treat2 is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 
> 1.0e-09)
(MWFE estimator converged in 10 iterations)
note: 1.ibc omitted because of collinearity
note: 1.treat2 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,559
Absorbing 2 HDFE groups                           F(  13,    744) =      32.69
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9216
                                                  Adj R-squared   =     0.8997
                                                  Within R-sq.    =     0.5170
Number of clusters (code) =        745Root MSE      =     0.0650

                     (Std. err. adjusted for 745 clusters in code)
--------------------------------------------------------------------------------
               |               Robust
       lever_w | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
         1.ibc |          0  (omitted)
      1.treat2 |          0  (omitted)
               |
    ibc#treat2 |
          1 1  |   .0109666   .0077777     1.41   0.159    -.0043022    .0262354
               |
        size_w |   -.004929   .0099703    -0.49   0.621    -.0245022    .0146442
      nfa_ta_w |   .1371436   .0258866     5.30   0.000      .086324    .1879631
     cash_ta_w |   .0873572   .0527998     1.65   0.098    -.0162971    .1910114
trade_credit_w |    .204013   .0421171     4.84   0.000     .1213304    .2866955
  sales_grow_w |  -.0072218    .003361    -2.15   0.032      -.01382   -.0006235
         roa_w |  -.0428388   .0298777    -1.43   0.152    -.1014933    .0158158
          pb_w |  -5.14e-06   .0011595    -0.00   0.996    -.0022813    .0022711
      cfo_ta_w |     -.1118   .0203783    -5.49   0.000    -.1518058   -.0717941
     rddcc_dum |  -.0096273   .0058958    -1.63   0.103    -.0212018    .0019471
       div_dum |  -.0049204   .0053542    -0.92   0.358    -.0154317    .0055908
       nw_ta_w |  -.5787685   .0397092   -14.58   0.000     -.656724    -.500813
           age |  -.0145416   .0329906    -0.44   0.660    -.0793074    .0502242
         _cons |   .5850335   .1386969     4.22   0.000     .3127495    .8573174
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
          ff48#year |       239           0         239     |
         code |       745         745           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
Q1. In the above output ibc is the time dummy (1 of years 2017-19; 0 otherwise) and treat2 is the treatment dummy. How do I interpret ibc#treat2? Can I say that there is no significant difference between treatment and control groups with respect to their leverage (dependent variable) after the passage of law? Did I interpret less or more?

I have seen the use of -margins-command and I used it here without much knowledge and here are my outputs

Code:
margins treat2#ibc, noestimcheck

Predictive margins                                       Number of obs = 4,559
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
  treat2#ibc |
        0 0  |    .300477   .0015934   188.58   0.000      .297354       .3036
        0 1  |    .300477   .0015934   188.58   0.000      .297354       .3036
        1 0  |    .300477   .0015934   188.58   0.000      .297354       .3036
        1 1  |   .3114435   .0061843    50.36   0.000     .2993226    .3235645
------------------------------------------------------------------------------

. margins treat2, dydx(ibc) noestimcheck

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  1.ibc

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.ibc        |  (base outcome)
-------------+----------------------------------------------------------------
1.ibc        |
      treat2 |
          0  |          0  (omitted)
          1  |   .0109666   .0077777     1.41   0.159    -.0042774    .0262105
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. margins ibc, dydx(treat2), noestimcheck
invalid 'noestimcheck' 
r(198);
l have seen Richard Williams excellent discussion on Margins, but if someone could help me interpret this margins output with respect to my specific example I can learn somewhat concretely.

Also why margins ibc, dydx(treat2), noestimcheck didnt work