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st: GMM error (bug in Stata?)


From   John Antonakis <John.Antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   st: GMM error (bug in Stata?)
Date   Sat, 29 Oct 2011 18:33:19 +0200

Hi:

I am trying to estimate "stacked" models using -gmm-. When I estimate the two models separately, things work fine (see code on the bottom of my e-mail). However, when I run the models jointly, with the following code...........

gmm  ///
(eq1: y - {b1}*x_style1 - {b2}*x_style2- {b3}*x_style3- {b4}*x_style4- ///
{b5}*x_style5- {b6}*x_style6 - {b7}*x_style7- {b8}*x_style8- {b9}*x_style9- /// {b10}*x_style10- {b11}*x_style11- {b12}*x_style12- {b13}*x_style13- {b0}) ///

(eq2: y - {c1}*x_style1 - {c2}*x_style2- {c3}*x_style3- {c4}*x_style4- ///
{c5}*x_style5- {c6}*x_style6 - {c7}*x_style7- {c8}*x_style8- {c9}*x_style9- /// {c10}*x_style10- {c11}*x_style11- {c12}*x_style12- {c13}*x_style13- {c0}), ///

instruments(eq1: 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 ) ///

instruments(eq2: 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) ///

twostep winitial(unadjusted, indep) vce(cluster lead_n)

...........I get an error, which is a "strange" error, because the model is just identified (i.e,. I have an equal number of instruments as endogenous regressors):

. gmm  ///
> (eq1: y - {b1}*x_style1 - {b2}*x_style2- {b3}*x_style3- {b4}*x_style4- /// > {b5}*x_style5- {b6}*x_style6 - {b7}*x_style7- {b8}*x_style8- {b9}*x_style9- /// > {b10}*x_style10- {b11}*x_style11- {b12}*x_style12- {b13}*x_style13- {b0}) ///
>
Model not identified.  There are more parameters than instruments.
r(481);

Here's the code for the separate gmm models:

gmm  ///
(eq1: y - {b1}*x_style1 - {b2}*x_style2- {b3}*x_style3- {b4}*x_style4- ///
{b5}*x_style5- {b6}*x_style6 - {b7}*x_style7- {b8}*x_style8- {b9}*x_style9- /// {b10}*x_style10- {b11}*x_style11- {b12}*x_style12- {b13}*x_style13- {b0}), ///
instruments(eq1: 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 ) ///
twostep winitial(unadjusted, indep) vce(cluster lead_n)

and

gmm  ///
(eq1: y - {c1}*x_style1 - {c2}*x_style2- {c3}*x_style3- {c4}*x_style4- ///
{c5}*x_style5- {c6}*x_style6 - {c7}*x_style7- {c8}*x_style8- {c9}*x_style9- /// {c10}*x_style10- {c11}*x_style11- {c12}*x_style12- {c13}*x_style13- {c0}), /// instruments(eq1: 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) ///
twostep winitial(unadjusted, indep) vce(cluster lead_n)


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

Any ideas as to what the problem is?

Best regards,
John.

--
__________________________________________

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
__________________________________________

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