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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 17:28:43 +0100

Hi Maarten:

Yes! I mis-answered: The "no" was to say that the output I gave was from the non-stacked (and not the stacked models) and "not" from the stacked model. So "yes," the objective function is practically speaking zero.

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 17:18, Maarten Buis wrote:
1.7e-33 is 0.00000000000000000000000000000000017, so practically 0, so
I would answer yes rather than no.

On Sun, Oct 30, 2011 at 4:51 PM, John Antonakis<John.Antonakis@unil.ch>  wrote:
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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