Dear Stata Members
I have done a Difference in Difference model and I got the following output

Code:
reghdfe ltb_ta_w ///
> i.ibc2##i.treat2 ///
> size_w nfa_ta_w , a (co_code year)  ///
> vce(robust)
(dropped 54 singleton observations)
note: 1bn.ibc2 is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: 1bn.treat2 is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-0
> 9)
(MWFE estimator converged in 7 iterations)
note: 1.ibc2 omitted because of collinearity
note: 1.treat2 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,670
Absorbing 2 HDFE groups                           F(   3,   3031) =      21.63
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.8400
                                                  Adj R-squared   =     0.8063
                                                  Within R-sq.    =     0.0358
                                                  Root MSE        =     0.0714

------------------------------------------------------------------------------
             |               Robust
    ltb_ta_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      1.ibc2 |          0  (omitted)
    1.treat2 |          0  (omitted)
             |
ibc2#treat2 |
        1 1  |    .003701   .0056227     0.66   0.510    -.0073237    .0147258
             |
      size_w |   .0218235   .0069342     3.15   0.002     .0082273    .0354197
    nfa_ta_w |   .1341914   .0188946     7.10   0.000     .0971438    .1712389
       _cons |   -.065562   .0625945    -1.05   0.295    -.1882939      .05717
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     co_code |       629           0         629     |
        year |         8           1           7     |
-----------------------------------------------------+
ibc2 is the time dummy and treat2 is the dummy for treated and control groups. As my results indicate ibc2#treat2 turns to be statistically not significant. I have run this models with various controls but the interactions fails to get any significance. My doubts are
1. In this forum I have seen recommendations on standard errors to check whether there are any errors or not. One common saying is that inflated standard errors indicate some issues in the data while low standard errors are acceptable. However, as there are no hard and fast rules on standard errors, can someone help me in checking whether my standard errors are credible (ruling out possibilities of any errors in data or model).
2. Another approach is to look at confidence intrevals. Again does my confidence interval say something erroneous in model

I am mindful about Clyde Schechter remark on statistical significance but I wish to know what is the thin line that draws a boundary between p-hacking and ill-specified model etc