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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 17:34:54 +0100
Thanks Mark. Right; the models are just-identified.
I tried this with the 2sls estimator (i.e., with the "onestep") option
and without "twostep" and I get the same error:
"Model not identified. There are more parameters than instruments."
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 17:28, Schaffer, Mark E wrote:
> John,
>
> They're both exactly identified, as is the combined estimation (should
> have noticed that the first time). That's why the objective functions
> are going to exactly zero.
>
> It does look like a bug, but I wonder if it the bug might be triggered
> by your use of the twostep option. There's a one-sentence discussion of
> this in the Stata 11 manual on p. 583. Since it's exactly identified,
> twostep is irrelevant. What if you use the onestep option, or,
> equivalently, just omit twostep?
>
> --Mark
>
>> -----Original Message-----
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of
>> John Antonakis
>> Sent: 30 October 2011 15:52
>> To: [email protected]
>> Subject: Re: st: GMM error (bug in Stata?)
>>
>> 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/