Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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 17:34:54 +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: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>> John Antonakis
>> Sent: 30 October 2011 15:52
>> To: statalist@hsphsun2.harvard.edu
>> 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: 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/
>>>>
>>>> *
>>>> *   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/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index