[Eliana Melo]

Prof. Kripfganz,

Thank so much for your help. I achieved the same results with both commands:

Xtabond2:

xtabond2 pntbt L.pntbt hmcvi subnormal inadpf dec_apu, gmm (L.pntbt, lag(2 2) eq(d) collapse) gmm (L.pntbt, lag(2 2) eq(l) collapse) gmm (dec_apu, lag(1 1) eq(d) collapse) gmm (dec_apu, lag(1 1) eq(l) collapse)
iv(hmcvi subnormal inadpf, eq(d) mz) iv(hmcvi subnormal inadpf, eq(l) mz) twostep robust

xtdpdgmm:

xtdpdgmm pntbt L.pntbt hmcvi subnormal inadpf dec_apu, gmmiv(L.pntbt, lag(2 2) m(d) collapse) gmmiv(L.pntbt, lag(2 2) m(l) diff collapse) gmmiv(dec_apu, lag(1 1) m(d) collapse) gmmiv(dec_apu, lag(1 1) m(l) diff collapse) iv(hmcvi subnormal inadpf, m(d) diff) iv(hmcvi subnormal inadpf, m(l)) twostep vce(r)

And I also tested the serial correlation of the term error:

estat serial

Arellano-Bond test for autocorrelation of the first-differenced residuals
H0: no autocorrelation of order 1:     z =   -2.2844   Prob > |z|  =    0.0223
H0: no autocorrelation of order 2:     z =    1.5033   Prob > |z|  =    0.1328

Hansen test:

 estat overid

Sargan-Hansen test of the overidentifying restrictions
H0: overidentifying restrictions are valid

2-step moment functions, 2-step weighting matrix       chi2(5)     =    5.2986
                                                       Prob > chi2 =    0.3805

2-step moment functions, 3-step weighting matrix       chi2(5)     =    5.5599
                                                       Prob > chi2 =    0.3514

Then the results in the first test means the absence of the second-order serial correlation in disturbances. And the results with Hansen test means that there is not problem of overidentifying restrictions. Is it right?
Which is the most appropriate test of overindetifying restrictions to show in the results, 2-step weighting matrix or 3-step weighting matrix?


Once again, thank very much!

[Sebastian Kripfganz]

You are still using different lags in the two specifications. If you want to use the first lag of the lagged dependent variable in both specifications, then you need to modify the xtabond2 command line:

xtabond2 ..., gmm(L.pntbt, lag(1 1) eq(d) collapse) ...
xtdpdgmm ..., gmmiv(L.pntbt, lag(1 1) m(d) collapse) ...

and similarly in all other gmm() options!

A large coefficent of the lagged dependent variable is not necessarily a problem. You could check for neglected dynamics by testing for serial correlation of the error term with

estat serial

after the xtdpdgmm command.

[Eliana Melo]

Thank so much for your help. I didn't consider that xtabond2 by default applies a first-difference transformation to the instruments in iv.

I have modified the command according to the second option you mentioned and I still have different results for both command. I also added mz suboption to the xtabond2. Regarding the level model, do you advise creating first-differenced instruments for the level model? I ran without the first-difference for the level model.


Xtabond2:

xtabond2 pntbt L.pntbt hmcvi subnormal inadpf dec_apu, gmm (L.pntbt, lag(2 2) eq(d) collapse) gmm (L.pntbt, lag(2 2) eq(l) collapse) gmm (dec_apu, lag(2 2) eq(d) collapse) gmm (dec_apu, lag(1 1) eq(l) collapse) iv(hmcvi subnormal inadpf, eq(d) mz) iv(hmcvi subnormal inadpf, eq(l) mz) twostep robust

Results:

 xtabond2 pntbt L.pntbt hmcvi subnormal inadpf dec_apu, gmm (L.pntbt, lag(2 2) eq(d) collapse) gmm (L.pntbt, lag(2 2) eq(l) collapse) g
> mm (dec_apu, lag(2 2) eq(d) collapse) gmm (dec_apu, lag(1 1) eq(l) collapse) iv(hmcvi subnormal inadpf, eq(d) mz) iv(hmcvi subnormal i
> nadpf, eq(l) mz) twostep robust
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.

Dynamic panel-data estimation, two-step system GMM
------------------------------------------------------------------------------
Group variable: id                              Number of obs      =       423
Time variable : ano                             Number of groups   =        33
Number of instruments = 11                      Obs per group: min =         9
Wald chi2(5)  =   1161.71                                      avg =     12.82
Prob > chi2   =     0.000                                      max =        13
------------------------------------------------------------------------------
             |              Corrected
       pntbt |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       pntbt |
         L1. |   .7842838   .0743589    10.55   0.000     .6385431    .9300246
             |
       hmcvi |   .0003996   .0003474     1.15   0.250    -.0002814    .0010805
   subnormal |   .2959394   .1958883     1.51   0.131    -.0879946    .6798734
      inadpf |   .8593435   .2568274     3.35   0.001     .3559712    1.362716
     dec_apu |   .0012405   .0009055     1.37   0.171    -.0005343    .0030152
       _cons |  -.0543653   .0160513    -3.39   0.001    -.0858253   -.0229053
------------------------------------------------------------------------------
Instruments for first differences equation
  Standard
    D.(hmcvi subnormal inadpf), missing recoded as zero
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    L2.dec_apu collapsed
    L2.L.pntbt collapsed
Instruments for levels equation
  Standard
    hmcvi subnormal inadpf, missing recoded as zero
    _cons
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    DL.dec_apu collapsed
    DL2.L.pntbt collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z =  -2.44  Pr > z =  0.015
Arellano-Bond test for AR(2) in first differences: z =   1.56  Pr > z =  0.118
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(5)    =   5.62  Prob > chi2 =  0.345
  (Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(5)    =   5.95  Prob > chi2 =  0.311
  (Robust, but weakened by many instruments.)

Difference-in-Hansen tests of exogeneity of instrument subsets:
  GMM instruments for levels
    Hansen test excluding group:     chi2(3)    =   2.39  Prob > chi2 =  0.496
    Difference (null H = exogenous): chi2(2)    =   3.56  Prob > chi2 =  0.168
  gmm(L.pntbt, collapse eq(diff) lag(2 2))
    Hansen test excluding group:     chi2(4)    =   3.05  Prob > chi2 =  0.550
    Difference (null H = exogenous): chi2(1)    =   2.90  Prob > chi2 =  0.089
  gmm(L.pntbt, collapse eq(level) lag(2 2))
    Hansen test excluding group:     chi2(4)    =   5.03  Prob > chi2 =  0.284
    Difference (null H = exogenous): chi2(1)    =   0.92  Prob > chi2 =  0.339
  gmm(dec_apu, collapse eq(diff) lag(2 2))
    Hansen test excluding group:     chi2(4)    =   2.64  Prob > chi2 =  0.619
    Difference (null H = exogenous): chi2(1)    =   3.30  Prob > chi2 =  0.069
  gmm(dec_apu, collapse eq(level) lag(1 1))
    Hansen test excluding group:     chi2(4)    =   4.48  Prob > chi2 =  0.345
    Difference (null H = exogenous): chi2(1)    =   1.47  Prob > chi2 =  0.226
  iv(hmcvi subnormal inadpf, mz eq(diff))
    Hansen test excluding group:     chi2(2)    =   3.01  Prob > chi2 =  0.222
    Difference (null H = exogenous): chi2(3)    =   2.94  Prob > chi2 =  0.400
  iv(hmcvi subnormal inadpf, mz eq(level))
    Hansen test excluding group:     chi2(2)    =   0.15  Prob > chi2 =  0.928
    Difference (null H = exogenous): chi2(3)    =   5.80  Prob > chi2 =  0.122

xtdpdgmm:

xtdpdgmm pntbt L.pntbt hmcvi subnormal inadpf dec_apu, gmmiv(L.pntbt, lag(1 1) m(d) collapse) gmmiv(L.pntbt, lag(1 1) m(l) collapse) gmmiv(dec_apu, lag(1 1) m(d) collapse) gmmiv(dec_apu, lag(0 0) m(l) collapse) iv(hmcvi subnormal inadpf, m(d) diff) iv(hmcvi subnormal inadpf, m(l)) twostep vce(r)

Results:

xtdpdgmm pntbt L.pntbt hmcvi subnormal inadpf dec_apu, gmmiv(L.pntbt, lag(1 1) m(d) collapse) gmmiv(L.pntbt, lag(1 1) m(l) collapse) g
> mmiv(dec_apu, lag(1 1) m(d) collapse) gmmiv(dec_apu, lag(0 0) m(l) collapse) iv(hmcvi subnormal inadpf, m(d) diff) iv(hmcvi subnormal
> inadpf, m(l)) twostep vce(r)

Generalized method of moments estimation

Fitting full model:
Step 1         f(b) =  .00043768
Step 2         f(b) =  .26082684

Group variable: id                           Number of obs         =       423
Time variable: ano                           Number of groups      =        33

Moment conditions:     linear =      11      Obs per group:    min =         9
                    nonlinear =       0                        avg =  12.81818
                        total =      11                        max =        13

                                    (Std. Err. adjusted for 33 clusters in id)
------------------------------------------------------------------------------
             |              WC-Robust
       pntbt |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       pntbt |
         L1. |   .9087191   .0147842    61.47   0.000     .8797425    .9376956
             |
       hmcvi |   .0001282   .0001377     0.93   0.352    -.0001418    .0003982
   subnormal |   .0723061   .0550229     1.31   0.189    -.0355368     .180149
      inadpf |   .5317917    .232069     2.29   0.022     .0769449    .9866385
     dec_apu |   .0006042   .0002017     3.00   0.003     .0002089    .0009995
       _cons |  -.0287216   .0094184    -3.05   0.002    -.0471813   -.0102618
------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
 1, model(diff):
   L1.L.pntbt
 2, model(level):
   L1.L.pntbt
 3, model(diff):
   L1.dec_apu
 4, model(level):
   dec_apu
 5, model(diff):
   D.hmcvi D.subnormal D.inadpf
 6, model(level):
   hmcvi subnormal inadpf
 7, model(level):
   _cons

I am a bit worried because with the xtdpdgmm command the lagged dependent variable is almost 1. There is a misspecification or omitted dynamics?


Thanks!

[Sebastian Kripfganz]

Unless you are using xtabond2 with the orthogonal option, which does not seem to be the case here, you need to specify the exact same lag orders for the instruments in the two command lines. Moreover, xtabond2 by default applies a first-difference transformation to the instruments in iv(hmcvi subnormal inadpf, eq(d)), while xtdpdgmm does not. You either need to add the passthru suboption in the xtabond2 command line, or the diff option with xtdpdgmm, i.e.

xtabond2 ..., ... iv(hmcvi subnormal inadpf, eq(d) passthru)
xtdpdgmm ..., ... iv(hmcvi subnormal inadpf, m(d))

or

xtabond2 ..., ... iv(hmcvi subnormal inadpf, eq(d))
xtdpdgmm ..., ... iv(hmcvi subnormal inadpf, m(d) diff)

For the level model, no transformation is applied by default with either command. If you want first-differenced instruments for the level model, you need to modify the xtabond2 command as follows:

xtabond2 ..., ... iv(D.hmcvi D.subnormal D.inadpf, eq(l))
xtdpdgmm ..., ... iv(hmcvi subnormal inadpf, m(l) diff)

If there are still remaining differences, try adding the mz suboption to the xtabond2 iv() options.

[Eliana Melo]

Hi, I want to estimate a system gmm with xtdpdgmm in stata 14.2. My dependent variable is the percentage of Non-Technical Losses in distribution of electricity (pntbt) for 33 utilities and the period is 2003-2016. Is an unbalanced panel. I have suspect of endogeneity of an explanatory variable: duration of electric distribution outages (dec_apurado). The another variables are: hmcvi (rate of homicides), subnormal (proportion of the urban population living in slums) and inadpf (rate of people credit default).

I have the following routine with xtabond2:

xtabond2 pntbt L.pntbt hmcvi subnormal inadpf dec_apu, gmm (L.pntbt, lag(2 2) eq(d) collapse) gmm (L.pntbt, lag(2 2) eq(l) collapse) gmm (dec_apu, lag(2 2) eq(d) collapse) gmm (dec_apu, lag(1 1) eq(l)  collapse) iv(hmcvi subnormal inadpf, eq(d)) iv(hmcvi subnormal inadpf, eq(l)) twostep robust

Then, I am trying to replicate the results with xtdpdgmm command:

xtdpdgmm pntbt L.pntbt hmcvi subnormal inadpf dec_apu, gmmiv(L.pntbt, lag(1 1) m(d) collapse) gmmiv(L.pntbt, lag(1 1) m(l) collapse) gmmiv(dec_apu, lag(1 1) m(d) collapse) gmmiv(dec_apu, lag(0 0) m(l) collapse) iv(hmcvi subnormal inadpf, m(d)) iv(hmcvi subnormal inadpf, m(l) diff) twostep vce(r)

The results are different with each command. I read the documentation of xtpdgmm, but I think I am missing something. Any comment would be valuable.

[Sebastian Kripfganz]

There is a new update to version 2.3.4 on my website:

net install xtdpdgmm, from(http://www.kripfganz.de/stata/) replace

This version fixes a bug that produced an incorrect list of instruments in the output footer and incorrectly labelled the instruments generated by the postestimation command predict, iv. This bug only bit if a static model was estimated with GMM-type instruments. If the model included a lag of the dependent or independent variables, then the problem did not occur. This bug did not affect any of the computations. It was just a matter of displaying the correct list of instruments.

[Chhavi Jatana]

Alright, thank you so much for the clarification.

[Sebastian Kripfganz]

I have five years study period, so I have formed only four dummy variables (first year is taken as a base year) based on n-1 formula. Do I still need to drop one of the year dummies?

Yes, because the lagged dependent variable is effectively reducing your estimation sample by one additional study period.

[Chhavi Jatana]

First of all, thank you for prompt response.

Sir, I have five years study period, so I have formed only four dummy variables (first year is taken as a base year) based on n-1 formula. Do I still need to drop one of the year dummies?

I am a beginner and facing a lot of problems while applying the system GMM for my thesis, so might ask some silly questions. Apologies in advance. I will follow your advice on the dynamic model. I hope it helps.

[Sebastian Kripfganz]

The reason for the non-convergence with the nl(noserial) option is that the perfect collinearity among your time dummies. You need to manually drop one of those time dummies.

A random-effects (or fixed-effects) regression makes much stronger assumptions that effectively lead to much stronger instruments. In particular, all variables are assumed to be strictly exogenous.

You could start with a dynamic model that assumes all variables (other than the lagged dependent variable) being strictly exogenous and then relax this assumption for one variable after the other to see whether a particular variable is causing the trouble. I.e. start with the following specification:

xtdpdgmm TC L.TC ROEP ROET3 T3 LFSIZE LFAGE LEV RISK CEOD BSZE IND_P ID* YD2 YD3 YD4, twostep vce(cluster cid) collapse gmmiv(L.TC, lag(0 0) model(fodev)) gmmiv(ROEP ROET3 T3 LFSIZE LFAGE LEV RISK CEOD BSZE IND_P, lag(0 1) model (fodev)) gmmiv(ROEP ROET3 T3 LFSIZE LFAGE LEV RISK CEOD BSZE IND_P, lag(0 0) model(mdev)) iv(ID* YD2 YD3 YD4, model (level)) nofooter

The part in red are the extra instruments valid only under strict exogeneity.

[Chhavi Jatana]

Dear Sir,

nl(noserial) option is not working

xtdpdgmm TC L.TC ROEP ROET3 T3 LFSIZE LFAGE LEV RISK CEOD BSZE IND_P ID* YD*,twostep vce(cluster cid) nl(noserial) gmmiv (L
> .TC, lag(0 0) collapse model (fodev)) gmmiv (ROEP, lag(0 1) collapse model (fodev)) gmmiv (ROET3, lag(0 1) collapse model (
> fodev)) gmmiv (T3, lag(0 1) collapse model (fodev)) gmmiv (LFSIZE, lag(0 1) collapse model (fodev)) gmmiv (LFAGE, lag(0 1)
> collapse model (fodev)) gmmiv (LEV, lag(0 1) collapse model (fodev)) gmmiv (RISK, lag(0 1) collapse model (fodev)) gmmiv (B
> SZE, lag(0 1) collapse model (fodev)) gmmiv (IND_P, lag(0 1) collapse model (fodev)) gmmiv (CEOD, lag(0 1) collapse model (
> fodev)) gmmiv (ID*, lag(0 0) collapse model (level))gmmiv (YD*, lag(0 0) collapse model (level)) nofootnote

Generalized method of moments estimation

Fitting full model:

Step 1:
initial:       f(b) =   18948478
alternative:   f(b) =   18911537
rescale:       f(b) =  7130192.3
Iteration 0:   f(b) =  7130192.3  (not concave)
Iteration 1:   f(b) =  949992.37  (not concave)
Iteration 2:   f(b) =  278578.86  (not concave)
Iteration 3:   f(b) =  151619.85  (not concave)
Iteration 4:   f(b) =  120342.38  (not concave)
Iteration 5:   f(b) =  97568.645  (not concave)
Iteration 6:   f(b) =  81329.831  (not concave)
Iteration 7:   f(b) =  70476.142  (not concave)
Iteration 8:   f(b) =  59107.138  (not concave)
Iteration 9:   f(b) =  52308.153  (not concave)
Iteration 10:  f(b) =  43053.096  (not concave)
Iteration 11:  f(b) =  33223.071  (not concave)
Iteration 12:  f(b) =  29835.377  (not concave)
Iteration 13:  f(b) =  16909.705  (not concave)
Iteration 14:  f(b) =  15002.974  (not concave)
Iteration 15:  f(b) =  14232.683  (not concave)
Iteration 16:  f(b) =  7718.8442  (not concave)
Iteration 17:  f(b) =  7532.1743  (not concave)
Iteration 18:  f(b) =  7366.5578  (not concave)
Iteration 19:  f(b) =  7217.5852  (not concave)
Iteration 20:  f(b) =  7078.5422  (not concave)
Iteration 21:  f(b) =  6947.9369  (not concave)
Iteration 22:  f(b) =  6826.4571  (not concave)
Iteration 23:  f(b) =  6711.9993  (not concave)
Iteration 24:  f(b) =  6604.6715  (not concave)
Iteration 25:  f(b) =  6503.2638  (not concave)
Iteration 26:  f(b) =  6408.0337  (not concave)
Iteration 27:  f(b) =  6317.8641  (not concave)
Iteration 28:  f(b) =  6232.9867  (not concave)
Iteration 29:  f(b) =  6152.4831  (not concave)
Iteration 30:  f(b) =  6076.5509  (not concave)
Iteration 31:  f(b) =  6004.4055  (not concave)
Iteration 32:  f(b) =  5936.2248  (not concave)
Iteration 33:  f(b) =  5871.3394  (not concave)
Iteration 34:  f(b) =  5809.9042  (not concave)
Iteration 35:  f(b) =  5751.3458  (not concave)
Iteration 36:  f(b) =  5695.8011  (not concave)
Iteration 37:  f(b) =  5642.7766  (not concave)
Iteration 38:  f(b) =   5592.393  (not concave)
Iteration 39:  f(b) =  5544.2241  (not concave)
Iteration 40:  f(b) =  5498.3769  (not concave)
Iteration 41:  f(b) =  5454.4817  (not concave)
Iteration 42:  f(b) =  5412.6337  (not concave)
Iteration 43:  f(b) =  5372.5111  (not concave)
Iteration 44:  f(b) =  5334.1987  (not concave)
Iteration 45:  f(b) =  5297.4154  (not concave)
Iteration 46:  f(b) =  5262.2372  (not concave)
Iteration 47:  f(b) =  5228.4177  (not concave)
Iteration 48:  f(b) =  5196.0252  (not concave)
Iteration 49:  f(b) =  5164.8429  (not concave)
Iteration 50:  f(b) =  5134.9323  (not concave)
Iteration 51:  f(b) =  5106.1022  (not concave)
Iteration 52:  f(b) =  5078.4083  (not concave)
Iteration 53:  f(b) =  5051.6812  (not concave)
Iteration 54:  f(b) =  5025.9715  (not concave)
Iteration 55:  f(b) =  5001.1288  (not concave)
Iteration 56:  f(b) =  4977.1991  (not concave)
Iteration 57:  f(b) =  4954.0487  (not concave)
Iteration 58:  f(b) =  4931.7194  (not concave)
Iteration 59:  f(b) =  4910.0918  (not concave)
Iteration 60:  f(b) =  4889.2043  (not concave)
Iteration 61:  f(b) =  4868.9499  (not concave)
Iteration 62:  f(b) =  4849.3638  (not concave)
Iteration 63:  f(b) =  4830.3502  (not concave)
Iteration 64:  f(b) =  4811.9413  (not concave)
Iteration 65:  f(b) =  4794.0509  (not concave)
Iteration 66:  f(b) =  4776.7087  (not concave)
Iteration 67:  f(b) =   4759.837  (not concave)
Iteration 68:  f(b) =  4743.4633  (not concave)
Iteration 69:  f(b) =  4727.5172  (not concave)
Iteration 70:  f(b) =  4712.0243  (not concave)
Iteration 71:  f(b) =  4696.9209  (not concave)
Iteration 72:  f(b) =  4682.2305  (not concave)
--Break--

These are the results from the Random effects model applied and most of the variables are significant. Only after applying system GMM, I am getting insignificant results. Can you please suggest some solution on the basis of these results?

xtreg TC ROEP ROET3 T3 LFSIZE LFAGE LEV RISK CEOD BSZE IND_P ID1 ID2 ID3 ID4 ID5 YD1 YD2 YD3 YD4, re vce (cluster cid)

Random-effects GLS regression                   Number of obs     =      1,005
Group variable: cid                             Number of groups  =        201

R-sq:                                           Obs per group:
     within  = 0.2511                                         min =          5
     between = 0.4991                                         avg =        5.0
     overall = 0.4145                                         max =          5

                                                Wald chi2(19)     =     110.21
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

                                  (Std. Err. adjusted for 201 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
          TC |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        ROEP |   11.81289   3.695961     3.20   0.001     4.568942    19.05684
       ROET3 |  -.1636423   .0533337    -3.07   0.002    -.2681745   -.0591101
          T3 |   .9268638   .5153631     1.80   0.072    -.0832294    1.936957
      LFSIZE |   21.30412   3.564797     5.98   0.000     14.31725    28.29099
       LFAGE |  -11.82151   7.810607    -1.51   0.130    -27.13002    3.486997
         LEV |  -20.22973   20.30682    -1.00   0.319    -60.03036     19.5709
        RISK |    1.05389   13.74748     0.08   0.939    -25.89068    27.99846
        CEOD |   23.02698   11.60022     1.99   0.047     .2909595      45.763
        BSZE |   3.099498   1.382514     2.24   0.025     .3898211    5.809175
       IND_P |   .9007423   .5228959     1.72   0.085    -.1241148    1.925599
         ID1 |  -47.37584   32.43792    -1.46   0.144     -110.953    16.20132
         ID2 |   3.698717   13.37388     0.28   0.782    -22.51361    29.91104
         ID3 |  -12.53249   14.97791    -0.84   0.403    -41.88865    16.82367
         ID4 |   2.917077   18.43619     0.16   0.874    -33.21719    39.05134
         ID5 |   30.07731   19.60296     1.53   0.125    -8.343787     68.4984
         YD1 |   1.671931   4.416635     0.38   0.705    -6.984515    10.32838
         YD2 |     1.6644   6.062894     0.27   0.784    -10.21865    13.54745
         YD3 |   4.077982    5.29307     0.77   0.441    -6.296245    14.45221
         YD4 |   15.10113   6.991724     2.16   0.031     1.397606    28.80466
       _cons |   -273.697   74.47474    -3.68   0.000    -419.6648   -127.7292
-------------+----------------------------------------------------------------
     sigma_u |  53.650479
     sigma_e |  56.558824
         rho |  .47362907   (fraction of variance due to u_i)
------------------------------------------------------------------------------

[Sebastian Kripfganz]

I am afraid I do not have a good answer. I notice that your standard errors are all very large which might be a consequence of weak instruments. You could try adding nonlinear moment conditions, e.g. option nl(noserial), although I am not sure if that will improve the situation.

[Chhavi Jatana]

Dear sir,

I want to apply the two-step system GMM to investigate the impact of ownership concentration on the CEO pay-performance relationship with 201 firms for 5 years of balanced panel data. I have applied the command given below. DV is TC; IDVs and control variables are ROEP T3 LFSIZE LFAGE LEV RISK CEOD BSZE IND_P; ROET3 is the interaction variable; ID* are 5 industry dummy variables and YD* are 4 year dummy variables

The results are not up to the mark- the p-value of the Hansen and Sargan test is very high; AR(1) and AR (2) both are insignificant and none of the coefficients are significant. I made some changes to the command like adding collapse to the equation to reduce number of instruments, changed the classification of variables from endogenous to exogenous but none worked.

Please suggest what can be done to meet all the assumptions along with retaining the significance of the coefficients.

xtdpdgmm TC L.TC ROEP ROET3 T3 LFSIZE LFAGE LEV RISK CEOD BSZE IND_P ID* YD*,twostep vce(cluster cid) gmmiv (L.TC, lag(0 0) collapse model (fodev)) gmmiv (ROEP, lag(0 1) collapse model (fodev)) gmmiv (ROET3, lag(0 1) collapse model (fodev)) gmmiv (T3, lag(0 1) collapse model (fodev)) gmmiv (LFSIZE, lag(0 1) collapse model (fodev)) gmmiv (LFAGE, lag(0 1) collapse model (fodev)) gmmiv (LEV, lag(0 1) collapse model (fodev)) gmmiv (RISK, lag(0 1) collapse model (fodev)) gmmiv (BSZE, lag(0 1) collapse model (fodev)) gmmiv (IND_P, lag(0 1) collapse model (fodev)) gmmiv (CEOD, lag(0 1) collapse model (fodev)) gmmiv (ID*, lag(0 0) collapse model (level)) gmmiv (YD*, lag(0 0) collapse model (level)) nofootnote
Generalized method of moments estimation

Fitting full model:
Step 1         f(b) =   380.3873
Step 2         f(b) =  .02331842

Group variable: cid                          Number of obs         =       804
Time variable: YEAR                          Number of groups      =       201

Moment conditions:     linear =      30      Obs per group:    min =         4
                    nonlinear =       0                        avg =         4
                        total =      30                        max =         4

                                  (Std. Err. adjusted for 201 clusters in cid)
------------------------------------------------------------------------------
             |              WC-Robust
          TC |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          TC |
         L1. |   .0704489   .2947275     0.24   0.811    -.5072064    .6481041
             |
        ROEP |  -2.161121   7.234564    -0.30   0.765    -16.34061    12.01836
       ROET3 |   .0486878   .1103199     0.44   0.659    -.1675352    .2649108
          T3 |  -1.268114   3.028676    -0.42   0.675    -7.204211    4.667982
      LFSIZE |   -17.6046   79.81283    -0.22   0.825    -174.0349    138.8257
       LFAGE |   121.1339   126.2982     0.96   0.338     -126.406    368.6738
         LEV |  -10.47428   151.6317    -0.07   0.945    -307.6669    286.7183
        RISK |  -25.74973   86.25241    -0.30   0.765    -194.8013    143.3019
        CEOD |  -70.64974   109.8793    -0.64   0.520    -286.0091    144.7096
        BSZE |  -1.578545   3.629034    -0.43   0.664    -8.691321    5.534232
       IND_P |  -1.513525    1.22047    -1.24   0.215    -3.905602    .8785514
         ID1 |   18.11663   97.57612     0.19   0.853     -173.129    209.3623
         ID2 |   7.156462   65.52682     0.11   0.913    -121.2737    135.5867
         ID3 |   16.69424   106.4915     0.16   0.875    -192.0252    225.4136
         ID4 |   -28.0001   83.33812    -0.34   0.737    -191.3398    135.3396
         ID5 |   42.28515   99.73981     0.42   0.672    -153.2013    237.7716
         YD1 |  -14.67081   27.28439    -0.54   0.591    -68.14724    38.80561
         YD2 |  -10.77153   18.99932    -0.57   0.571    -48.00952    26.46647
         YD3 |  -7.408057    9.57917    -0.77   0.439    -26.18288    11.36677
         YD4 |          0  (omitted)
       _cons |   11.46227   760.6348     0.02   0.988    -1479.355    1502.279
------------------------------------------------------------------------------

. estat overid

Sargan-Hansen test of the overidentifying restrictions
H0: overidentifying restrictions are valid

2-step moment functions, 2-step weighting matrix       chi2(10)    =    4.6870
                                                       Prob > chi2 =    0.9111

2-step moment functions, 3-step weighting matrix       chi2(10)    =    6.3643
                                                       Prob > chi2 =    0.7838

. estat serial

Arellano-Bond test for autocorrelation of the first-differenced residuals
H0: no autocorrelation of order 1:     z =   -0.3265   Prob > |z|  =    0.7440
H0: no autocorrelation of order 2:     z =   -0.3837   Prob > |z|  =    0.7012

Thanks in advance!

[Sebastian Kripfganz]

I cannot say much beyond what Jan Kiviet writes in his paper, and there is no safe zone that I feel confident laying out here. Eventually, it remains a matter of judgment depending on the particular data and application.

[John Sgr]

I see, this paper says that the perceived power of the test matters to judge p value of 0.15 for the Hansen test. If I revised my model to get higher p-value, which interval would provide at least safe zone?