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Re: st: GMM error (bug in Stata?)
From
John Antonakis <[email protected]>
To
[email protected]
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<[email protected]> 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: [email protected]
[mailto:[email protected]] On Behalf Of
John Antonakis
Sent: 30 October 2011 15:01
To: [email protected]
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: [email protected]
>> [mailto:[email protected]] On Behalf
Of>> John Antonakis>> Sent: 30 October 2011 05:54>> To:
[email protected]>> 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>>
<[email protected]> 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/