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Re: st: GMM error (bug in Stata?)
From
Maarten Buis <[email protected]>
To
[email protected]
Subject
Re: st: GMM error (bug in Stata?)
Date
Sun, 30 Oct 2011 17:18:39 +0100
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/
>
--
--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://www.maartenbuis.nl
--------------------------
*
* 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/