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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 16:51:33 +0100

Hi:

No; the output I gave was from when I don't stack. Here it is again:

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

And the second model:

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

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 16:34, Schaffer, Mark E wrote:
John,

When you don't stack, do you get nonzero values for the maximized GMM
objective functions?

--Mark

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
John Antonakis
Sent: 30 October 2011 15:01
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: GMM error (bug in Stata?)

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".
  >>
  >>  *
  >>  *   For searches and help try:
  >>  *   http://www.stata.com/help.cgi?search
  >>  *   http://www.stata.com/support/statalist/faq
  >>  *   http://www.ats.ucla.edu/stat/stata/

*
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