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RE: st: GMM error (bug in Stata?)
From
"Schaffer, Mark E" <[email protected]>
To
<[email protected]>
Subject
RE: st: GMM error (bug in Stata?)
Date
Sun, 30 Oct 2011 15:34:22 -0000
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/
>
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*
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