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

 From John Antonakis 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
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
__________________________________________

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

(Std. Err. adjusted for 418 clusters in
------------------------------------------------------------------------------
|               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

(Std. Err. adjusted for 418 clusters in
------------------------------------------------------------------------------
|               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
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
__________________________________________

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
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
__________________________________________

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
Number of obs
>>    =    3344
>>    GMM weight matrix:     Cluster (lead_n)
>>
>>                                    (Std. Err. adjusted for 418
>>    clusters in
>>    --------------------------------------------------------------
>>    ----------------
>>                  |               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
Number of obs
>>    =    3344
>>    GMM weight matrix:     Cluster (lead_n)
>>
>>                                    (Std. Err. adjusted for 418
>>    clusters in
>>    --------------------------------------------------------------
>>    ----------------
>>                  |               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
>>    __________________________________________
>>
>>
>>    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/

*
*   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/

```
```*
*   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/

```
```

```
```*
*   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/
```