Re: st: GMM error (bug in Stata?)

[John Antonakis <John.Antonakis@unil.ch>]

Hi Stas (and Cam):

Thanks for the follow-up but its not the number of clusters that is 
causing the problem; I have 418 of them (refers to the output of the 
first note:
Here is the output from the first gmm estimation:
Step 1
Iteration 0:   GMM criterion Q(b) =  13.184222
Iteration 1:   GMM criterion Q(b) =  1.497e-26
Iteration 2:   GMM criterion Q(b) =  4.387e-32

Step 2
Iteration 0:   GMM criterion Q(b) =  4.314e-33
Iteration 1:   GMM criterion Q(b) =  4.314e-33  (backed up)

GMM estimation

Number of parameters =  14
Number of moments    =  14
Initial weight matrix: Unadjusted                     Number of obs  
=    3344
GMM weight matrix:     Cluster (lead_n)

                               (Std. Err. adjusted for 418 clusters in 
lead_n)
------------------------------------------------------------------------------
             |               Robust
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. 
Interval]
-------------+----------------------------------------------------------------
         /b1 |   1.049204   .0549893    19.08   0.000     .9414266    
1.156981
         /b2 |   1.078344   .0586466    18.39   0.000     .9633983    
1.193289
         /b3 |   .9043237   .0616768    14.66   0.000     .7834394    
1.025208
         /b4 |    1.04687   .0528909    19.79   0.000     .9432057    
1.150534
         /b5 |   1.043876   .0569363    18.33   0.000     .9322833    
1.155469
         /b6 |    1.01851   .0592967    17.18   0.000     .9022906    
1.134729
         /b7 |   .9258437   .0602654    15.36   0.000     .8077256    
1.043962
         /b8 |   .9485584   .0553715    17.13   0.000     .8400322    
1.057085
         /b9 |   1.066044   .0601146    17.73   0.000     .9482216    
1.183867
        /b10 |   1.075929   .0577217    18.64   0.000     .9627967    
1.189062
        /b11 |   1.017601   .0614807    16.55   0.000     .8971007    
1.138101
        /b12 |  -.9610472   .0526738   -18.25   0.000    -1.064286   
-.8578085
        /b13 |  -.9627249   .0589321   -16.34   0.000     -1.07823   
-.8472202
         /b0 |  -.1096011   .0587362    -1.87   0.062    -.2247219    
.0055198
------------------------------------------------------------------------------
Instruments for equation 1: x_fe1 x_fe2 x_fe3 x_fe4 x_fe5 x_fe6 x_fe7 
x_fe8 x_fe9 x_fe10
    x_fe11 x_fe12 x_fe13 _cons

Here's the output from the second gmm estimation:

Step 1
Iteration 0:   GMM criterion Q(b) =  12.045213
Iteration 1:   GMM criterion Q(b) =  2.209e-26
Iteration 2:   GMM criterion Q(b) =  1.786e-32

Step 2
Iteration 0:   GMM criterion Q(b) =  1.883e-33
Iteration 1:   GMM criterion Q(b) =  1.656e-33

GMM estimation

Number of parameters =  14
Number of moments    =  14
Initial weight matrix: Unadjusted                     Number of obs  
=    3344
GMM weight matrix:     Cluster (lead_n)

                               (Std. Err. adjusted for 418 clusters in 
lead_n)
------------------------------------------------------------------------------
             |               Robust
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. 
Interval]
-------------+----------------------------------------------------------------
         /c1 |   .9598146   .0517448    18.55   0.000     .8583967    
1.061232
         /c2 |   .9256337   .0535588    17.28   0.000     .8206605    
1.030607
         /c3 |   .8305105   .0582733    14.25   0.000     .7162969    
.9447241
         /c4 |    .956631   .0482825    19.81   0.000     .8619991    
1.051263
         /c5 |   .9736638    .053159    18.32   0.000     .8694742    
1.077853
         /c6 |   .9493385   .0541098    17.54   0.000     .8432853    
1.055392
         /c7 |   .8518398   .0555893    15.32   0.000     .7428867    
.9607929
         /c8 |   .8813955    .051279    17.19   0.000     .7808906    
.9819004
         /c9 |   .9793823   .0518981    18.87   0.000      .877664    
1.081101
        /c10 |   .9923967   .0533734    18.59   0.000     .8877868    
1.097007
        /c11 |   .8911549   .0555809    16.03   0.000     .7822183    
1.000092
        /c12 |   -.865805   .0502334   -17.24   0.000    -.9642607   
-.7673493
        /c13 |  -.8909156   .0537489   -16.58   0.000    -.9962615   
-.7855697
         /c0 |  -.1046114   .0564682    -1.85   0.064    -.2152871    
.0060643
------------------------------------------------------------------------------
Instruments for equation 1: x_clus1 x_clus2 x_clus3 x_clus4 x_clus5 
x_clus6 x_clus7 x_clus8
    x_clus9 x_clus10 x_clus11 x_clus12 x_clus13 _cons

Best,
J.

__________________________________________

Prof. John Antonakis
Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor
The Leadership Quarterly
__________________________________________


On 30.10.2011 00:40, Stas Kolenikov wrote:
> John,
>
> how many clusters do you have? May be you are running out of clusters
> in estimation of the weight matrix if you have fewer clusters than
> parameters.
>
> On Sat, Oct 29, 2011 at 11:56 AM, John Antonakis 
<John.Antonakis@unil.ch> wrote:
>> The goal of my estimation procedure is to make cross model 
comparison, where
>> the model have different instruments ( and given the clustering I 
have, I
>> want to have a generalized Hausman test hence the use of gmm). I want to
>> show that the second stage estimates don't change when I change the
>> instruments....it's a simulation study I am working on, hence the
>> "strangeness".

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