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


From   John Antonakis <John.Antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: GMM error (bug in Stata?)
Date   Sun, 30 Oct 2011 16:00:44 +0100

Hi Mark:

I am unsure, particularly because gmm works when I don't stack the models. I have send the dataset and code to the Stata people to look at
(also, forgot to mention, I am using Stata 11).

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 12:47, Schaffer, Mark E wrote:
> John,
>
> I see from the output that after an iteration, the value of the GMM
> objective function becomes very small, e.g., 1.656e-33 ... in other
> words, zero.
>
> This could happen if the model is exactly identified, or (if I remember
> the discussion in Hall's GMM book correctly) if the rank of the VCV of
> moment conditions is PSD instead of PD.  Could either of these be the
> explanation?
>
> Cheers,
> Mark
>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>> John Antonakis
>> Sent: 30 October 2011 05:54
>> To: statalist@hsphsun2.harvard.edu
>> Subject: Re: st: GMM error (bug in Stata?)
>>
>> 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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