Dear Stata Experts,

I am dealing with a dynamic panel model in which the sample size is 100 firms and the time period is 5 years (2015-2020).

The model is presented below as:
​​​​​​
ROA_it=β_0+β_1ROAi_(t-1)+β_3Xit +β_4Cit + αi+ λt + ε_it (1)

WHERE:

DEPENDENT: ROA
ENDOGENEOUS: l.ROA
EXPLANATORY (X): CCC, DPO, DIO, DSO
CONTROL(C): debt_rat, curr_rat, firm_size, wcreq, grwce

All specifications of Eq. (1) are estimated with the GMM estimator system, using the Stata command xtabond2. I am using four study models for each explanatory variable and the results obtained for the first model are presented below:
Model 1
ROA=ROAt-1+ DSO + CURR_RAT + DEBT_RAT + FIRM_SIZE + WCREQ + GRWCE + lt


CODE: xtabond2 l(0/1).roa dso curr_rat debt_rat firm_size wcreq grwce yr2015-yr2020, gmm(roa, lag(1 5) collapse eq(diff)) gmm(dio dpo ccc curr_rat debt_rat firm_size wcreq grwce, lag(2 3) collapse equation(diff)) gmm(l2.(d.(dio dpo ccc curr_rat debt_rat firm_size wcreq grwce)), collapse equation(level)) iv( yr2015-yr2020, eq(level)) twostep nodiffsargan small

RESULTS:

xtabond2 l(0/1).roa dso curr_rat debt_rat firm_size wcreq grwce yr2015-yr2020, gmm(roa, lag(1 5)
> collapse eq(diff)) gmm(dio dpo ccc curr_rat debt_rat firm_size wcreq grwce, lag(2 3) collapse equ
> ation(diff)) gmm(l2.(d.(dio dpo ccc curr_rat debt_rat firm_size wcreq grwce)), collapse equation(l
> evel)) iv( yr2015-yr2020, eq(level)) twostep nodiffsargan small
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
yr2015 dropped due to collinearity
yr2019 dropped due to collinearity
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate optimal weighting matrix for two-step estimation.

Dynamic panel-data estimation, two-step system GMM
------------------------------------------------------------------------------
Group variable: idcompany Number of obs = 500
Time variable : t Number of groups = 100
Number of instruments = 34 Obs per group: min = 5
F(11, 99) = 58911.41 avg = 5.00
Prob > F = 0.000 max = 5 ------------------------------------------------------------------------------
roa | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
roa |
L1. | -.0143849 .0044405 -3.24 0.002 -.0231958 -.0055739
|
dso | .0265805 .0148492 1.79 0.077 -.0028835 .0560445
curr_rat | .0694694 .0726951 0.96 0.342 -.0747734 .2137121
debt_rat | .4320269 .2307227 1.87 0.064 -.0257769 .8898307
firm_size | -.9144968 .3667379 -2.49 0.014 -1.642184 -.1868092
wcreq | .9245246 1.918 0.48 0.631 -2.881203 4.730252
grwce | -2.497403 .8746998 -2.86 0.005 -4.232997 -.7618084
yr2016 | 1.28574 .8517638 1.51 0.134 -.4043443 2.975824
yr2017 | .0122819 .6098236 0.02 0.984 -1.19774 1.222304
yr2018 | -.969782 .5085728 -1.91 0.059 -1.978901 .0393367
yr2020 | -.5374178 .4408665 -1.22 0.226 -1.412193 .337357
_cons | 17.26591 8.101386 2.13 0.036 1.190997 33.34081
------------------------------------------------------------------------------
Warning: Uncorrected two-step standard errors are unreliable.

Instruments for first differences equation
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(2/3).(dio dpo ccc curr_rat debt_rat firm_size wcreq grwce) collapsed
L(1/5).roa collapsed
Instruments for levels equation
Standard
yr2015 yr2016 yr2017 yr2018 yr2019 yr2020
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
DL(1/4).(L2D.dio L2D.dpo L2D.ccc L2D.curr_rat L2D.debt_rat L2D.firm_size
L2D.wcreq L2D.grwce) collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z = -1.66 Pr > z = 0.096
Arellano-Bond test for AR(2) in first differences: z = -0.38 Pr > z = 0.706
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(22) = 21.39 Prob > chi2 = 0.497
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(22) = 12.85 Prob > chi2 = 0.937
(Robust, but weakened by many instruments.)

Having not implemented the robust option on my syntax, does it lead to any imperfections on my the results obtained ? Can we still accept the output even without implementing the robust option ?

Please note: When I include robust option on the syntax, I get invalid results from post-estimation diagnostics tests and on my endogeneous as well as my explanatory variables. Therefore, I would like to know whether can I keep the syntax used.

I would really appreciate your feedback on the results obtained with regards to the syntax implemented.