Dear Stata users,
I have a dynamic panel of 113 countries for 27 year, and I am using the system-GMM estimation. I use the code:

Code:
 xtabond2 gdpg l.gdpg school invest popl gs gc agd purge revolt assas gw ic et rt yr*, gmm(l.gdpg school invest popl gs gc agd purge revolt assas gw ic et rt, laglimit (1 2) eq(diff) collapse) gmm(l.gdpg school invest popl gs gc agd purge revolt assas gw ic et rt, laglimit(1 1) eq(level) collapse ) iv(yr*, eq(level)) twostep robust orthogonal small
where the dependent variable is GDP growth and the control variables is lag of GDP, education, investment and population, and the rest is variable that indicates domestic political conflict, I also created time dummies. The code pass the AR(2) test but not the Hansen test, it never went above 0.05

Code:
Dynamic panel-data estimation, two-step system GMM
------------------------------------------------------------------------------
Group variable: id                              Number of obs      =      1889
Time variable : t                               Number of groups   =       101
Number of instruments = 68                      Obs per group: min =         1
F(39, 100)    =     36.05                                      avg =     18.70
Prob > F      =     0.000                                      max =        26
------------------------------------------------------------------------------
             |              Corrected
        gdpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        gdpg |
         L1. |   .2960709   .0543676     5.45   0.000     .1882071    .4039348
             |
      school |  -.0040435   .0124473    -0.32   0.746    -.0287386    .0206515
      invest |   .1367087   .0499446     2.74   0.007     .0376202    .2357973
        popl |  -.5880374   .2726993    -2.16   0.033    -1.129065   -.0470097
          gs |  -.3794889   .2463908    -1.54   0.127    -.8683213    .1093434
          gc |  -.8519153   .3225023    -2.64   0.010    -1.491751   -.2120799
         agd |  -.0321581   .0485595    -0.66   0.509    -.1284987    .0641825
       purge |   .0580827   .2926748     0.20   0.843    -.5225757    .6387411
      revolt |   .1263746   .4156423     0.30   0.762    -.6982479    .9509971
       assas |   .0611081   .0599711     1.02   0.311    -.0578728    .1800889
          gw |   .0718747   .0280384     2.56   0.012     .0162473    .1275021
          ic |   .2486816   .2351381     1.06   0.293    -.2178256    .7151889
          et |   .1095639   .3872726     0.28   0.778    -.6587739    .8779017
          rt |   .1094858   .3044316     0.36   0.720    -.4944979    .7134694
        yr_2 |   1.260909   .6875653     1.83   0.070    -.1032015    2.625019
        yr_3 |   1.008915   .8446418     1.19   0.235    -.6668299    2.684661
        yr_4 |   .6752313   .7822947     0.86   0.390    -.8768191    2.227282
        yr_5 |   1.037699   .7106692     1.46   0.147    -.3722486    2.447646
        yr_6 |    .921716   .8239169     1.12   0.266    -.7129117    2.556344
        yr_7 |  -.0508602   .9714175    -0.05   0.958    -1.978125    1.876404
        yr_8 |   .6293482   .7320509     0.86   0.392      -.82302    2.081716
        yr_9 |   1.669619   .7356754     2.27   0.025     .2100595    3.129178
       yr_10 |   .6720811   .5619203     1.20   0.235    -.4427528    1.786915
       yr_11 |   .8680971    .577282     1.50   0.136    -.2772139    2.013408
       yr_12 |   1.071278   .6431222     1.67   0.099    -.2046578    2.347214
       yr_13 |   2.414394   .5913079     4.08   0.000     1.241256    3.587532
       yr_14 |   1.816109   .4850552     3.74   0.000     .8537729    2.778444
       yr_15 |   2.024707   .4181252     4.84   0.000     1.195159    2.854256
       yr_16 |    1.96759     .43334     4.54   0.000     1.107855    2.827324
       yr_18 |  -3.516978   .6232903    -5.64   0.000    -4.753569   -2.280388
       yr_19 |   3.040113   .6611827     4.60   0.000     1.728345     4.35188
       yr_20 |   .9749902   .6439827     1.51   0.133    -.3026531    2.252634
       yr_21 |   .5292614   .5967165     0.89   0.377    -.6546072     1.71313
       yr_22 |   .8616863   .6647106     1.30   0.198    -.4570806    2.180453
       yr_23 |   1.429101   .6963924     2.05   0.043     .0474787    2.810724
       yr_24 |    1.54518   .7890851     1.96   0.053    -.0203422    3.110702
       yr_25 |   .9606144   .7685481     1.25   0.214     -.564163    2.485392
       yr_26 |   1.369836   .6666083     2.05   0.042     .0473043    2.692368
       yr_27 |   1.355063   .7153157     1.89   0.061    -.0641025    2.774229
       _cons |  -4.633039   3.427836    -1.35   0.180    -11.43377     2.16769
------------------------------------------------------------------------------
Instruments for orthogonal deviations equation
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    L(1/2).(L.gdpg school invest popl gs gc agd purge revolt assas gw ic et
    rt) collapsed
Instruments for levels equation
  Standard
    yr_1 yr_2 yr_3 yr_4 yr_5 yr_6 yr_7 yr_8 yr_9 yr_10 yr_11 yr_12 yr_13 yr_14
    yr_15 yr_16 yr_17 yr_18 yr_19 yr_20 yr_21 yr_22 yr_23 yr_24 yr_25 yr_26
    yr_27
    _cons
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    DL.(L.gdpg school invest popl gs gc agd purge revolt assas gw ic et rt)
    collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z =  -4.74  Pr > z =  0.000
Arellano-Bond test for AR(2) in first differences: z =   0.52  Pr > z =  0.600
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(28)   = 129.13  Prob > chi2 =  0.000
  (Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(28)   =  40.74  Prob > chi2 =  0.057
  (Robust, but weakened by many instruments.)
Can someone please tell me what is the reason of this? How much should I value the Hansen test? is this a major problem?
I also tried further lags, it does solve the Hansen problem but it made all the variable insignificant so I dont think it is a good option.

Thank you for helping me.

Best,
Annika