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Re: st: Comparing coefficients from two ivregress models


From   John Antonakis <John.Antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Comparing coefficients from two ivregress models
Date   Sat, 17 Sep 2011 17:09:45 +0200

Pardon the typo.....it was "onestep" that I had written (and not "onstep"). Thus the syntax was:

gmm (eq1: turnover - {b1}*lmx - {b0}) ///
(eq2: turnover - {c1}*lmx - {c2}*l_incentives - {c3}*f_neuro - {c0}) /// (eq3: turnover - {d1}*lmx - {d2}*l_incentives - {d3}*l_iq - {d4}*c_policies - {d5}*f_neuro - {d0}), ///
    instruments(eq1: l_extra f_IQ f_consc) ///
    instruments(eq2: l_extra f_IQ f_consc l_incentives f_neuro) ///
instruments(eq3: l_extra f_IQ f_consc l_incentives l_iq c_policies f_neuro)
    onestep winitial(unadjusted, indep) vce(unadjusted)

The error still remains.

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 17.09.2011 17:02, John Antonakis wrote:
> Hi Tirthankar:
>
> In fact, I estimated the following:
>
> gmm (eq1: turnover - {b1}*lmx - {b0}) ///
> (eq2: turnover - {c1}*lmx - {c2}*l_incentives - {c3}*f_neuro - {c0}) /// > (eq3: turnover - {d1}*lmx - {d2}*l_incentives - {d3}*l_iq - {d4}*c_policies - {d5}*f_neuro - {d0}), ///
>     instruments(eq1: l_extra f_IQ f_consc) ///
>     instruments(eq2: l_extra f_IQ f_consc l_incentives f_neuro) ///
> instruments(eq3: l_extra f_IQ f_consc l_incentives l_iq c_policies f_neuro)
>     onstep winitial(unadjusted, indep) vce(unadjusted)
>
> When running the above, I then get (notice, it cuts it off after defining the equation 3 instruments):
>
> . gmm (eq1: turnover - {b1}*lmx - {b0}) ///
> > (eq2: turnover - {c1}*lmx - {c2}*l_incentives - {c3}*f_neuro - {c0}) /// > > (eq3: turnover - {d1}*lmx - {d2}*l_incentives - {d3}*l_iq - {d4}*c_policies - {d5}*f_neuro
> >  - {d0}), ///
> >     instruments(eq1: l_extra f_IQ f_consc) ///
> >         instruments(eq2: l_extra f_IQ f_consc l_incentives f_neuro) ///
> > instruments(eq3: l_extra f_IQ f_consc l_incentives l_iq c_policies f_neuro)
> initial weight matrix not positive definite
> r(506);
>
> end of do-file
>
> r(506);
>
> The data are generated from :
>
> clear
> set seed 1234
> set obs 1000
>
> gen l_extra = 50+ 3*rnormal()
> gen l_incentives = 10 + 3*rnormal()
> gen l_iq = 110 + 3*rnormal()
> gen c_policies = 20 + 3*rnormal()
> gen f_IQ = 105 + 3*rnormal()
> gen f_consc = 40 + 3*rnormal()
> gen f_neuro = 35 + 3*rnormal()
> gen lmx = -250+l_extra + l_incentives + l_iq + f_IQ + f_consc - f_neuro+ 3*rnormal() > gen turnover = +150 -l_incentives -l_iq - c_policies + f_neuro + 3*rnormal()
>
> 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 17.09.2011 16:57, Tirthankar Chakravarty wrote:
> > You appear to have not included the
> >
> > onestep winitial(unadjusted, indep) vce(unadjusted)
> >
> > option in your joint estimation.
> >
> > T
> >
> > On Sat, Sep 17, 2011 at 7:02 AM, John Antonakis <John.Antonakis@unil.ch> wrote:
> >> Hi:
> >>
> >> I am trying to use the procedure suggested by Tirthankar below. I have three > >> equations that I would like to "stack" and then make cross-equations tests. > >> When I estimate the three equations separately, things work well, as I show
> >> below:
> >>
> >> . *Eq 1 alone
> >> . gmm (turnover - {b1}*lmx - {b0}), ///
> >>>     instruments(l_extra f_IQ f_consc) ///
> >>>         onestep winitial(unadjusted, indep)  vce(unadjusted)
> >> Step 1
> >> Iteration 0:   GMM criterion Q(b) =  2025.9871
> >> Iteration 1:   GMM criterion Q(b) =  .06748029
> >> Iteration 2:   GMM criterion Q(b) =  .06748029
> >>
> >> GMM estimation
> >>
> >> Number of parameters =   2
> >> Number of moments    =   4
> >> Initial weight matrix: Unadjusted                     Number of obs  =
> >>  1000
> >>
> >> ------------------------------------------------------------------------------
> >>             |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> >> Interval]
> >> -------------+----------------------------------------------------------------
> >>         /b1 |   .0184804    .043515     0.42   0.671    -.0668075
> >>  .1037682
> >>         /b0 |    44.4598   1.313379    33.85   0.000     41.88563
> >>  47.03398
> >> ------------------------------------------------------------------------------
> >> Instruments for equation 1: l_extra f_IQ f_consc _cons
> >>
> >> .
> >> . *Eq 2 alone
> >> . gmm (turnover - {c1}*lmx - {c2}*l_incentives - {c3}*f_neuro - {c0}), ///
> >>>     instruments(l_extra f_IQ f_consc l_incentives f_neuro) ///
> >>>         onestep winitial(unadjusted, indep) vce(unadjusted)
> >> Step 1
> >> Iteration 0:   GMM criterion Q(b) =  2044.3282
> >> Iteration 1:   GMM criterion Q(b) =  .09468009
> >> Iteration 2:   GMM criterion Q(b) =  .09468009
> >>
> >> GMM estimation
> >>
> >> Number of parameters =   4
> >> Number of moments    =   6
> >> Initial weight matrix: Unadjusted                     Number of obs  =
> >>  1000
> >>
> >> ------------------------------------------------------------------------------
> >>             |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> >> Interval]
> >> -------------+----------------------------------------------------------------
> >>         /c1 |  -.0010489   .0324056    -0.03   0.974    -.0645627
> >>  .0624648
> >>         /c2 |  -.9454141   .0641511   -14.74   0.000    -1.071148
> >> -.8196802
> >>         /c3 |   1.026038   .0621628    16.51   0.000     .9042014
> >>  1.147875
> >>         /c0 |   18.40918   2.636117     6.98   0.000     13.24249
> >>  23.57588
> >> ------------------------------------------------------------------------------ > >> Instruments for equation 1: l_extra f_IQ f_consc l_incentives f_neuro _cons
> >>
> >> .
> >> . *Eq 3 alone
> >> . gmm (turnover - {d1}*lmx - {d2}*l_incentives - {d3}*l_iq - {d4}*c_policies
> >> - {d5}*f_neuro - {d
> >>> 0}), ///
> >>> instruments(l_extra f_IQ f_consc l_incentives l_iq c_policies f_neuro)
> >>> ///
> >>>         onestep winitial(unadjusted, indep) vce(unadjusted)
> >> Step 1
> >> Iteration 0:   GMM criterion Q(b) =  2062.4499
> >> Iteration 1:   GMM criterion Q(b) =  .00820186
> >> Iteration 2:   GMM criterion Q(b) =  .00820186  (backed up)
> >>
> >> GMM estimation
> >>
> >> Number of parameters =   6
> >> Number of moments    =   8
> >> Initial weight matrix: Unadjusted                     Number of obs  =
> >>  1000
> >>
> >> ------------------------------------------------------------------------------
> >>             |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> >> Interval]
> >> -------------+----------------------------------------------------------------
> >>         /d1 |  -.0173308   .0183817    -0.94   0.346    -.0533583
> >>  .0186967
> >>         /d2 |  -.9578794   .0357112   -26.82   0.000    -1.027872
> >> -.8878869
> >>         /d3 |  -.9651611   .0365803   -26.38   0.000    -1.036857
> >>  -.893465
> >>         /d4 |   -1.02468   .0292714   -35.01   0.000    -1.082051
> >> -.9673096
> >>         /d5 |   1.000026   .0346869    28.83   0.000     .9320408
> >>  1.068011
> >>         /d0 |   146.6398   3.823647    38.35   0.000     139.1456
> >> 154.134
> >> ------------------------------------------------------------------------------
> >> Instruments for equation 1: l_extra f_IQ f_consc l_incentives l_iq
> >> c_policies f_neuro _cons
> >>
> >>
> >> However, when I estimate them all together I get and error with respect to
> >> the weight matrix not being positive-definite:
> >>
> >>
> >> . gmm (eq1: turnover - {b1}*lmx - {b0}) ///
> >>> (eq2: turnover - {c1}*lmx - {c2}*l_incentives - {c3}*f_neuro - {c0})
> >>> ///
> >>>     (eq3: turnover - {d1}*lmx - {d2}*l_incentives - {d3}*l_iq -
> >>> {d4}*c_policies - {d5}*f_neuro
> >>>  - {d0}), ///
> >>>     instruments(eq1: l_extra f_IQ f_consc) ///
> >>> instruments(eq2: l_extra f_IQ f_consc l_incentives f_neuro) /// > >>> instruments(eq3: l_extra f_IQ f_consc l_incentives l_iq c_policies
> >>> f_neuro)
> >> initial weight matrix not positive definite
> >>
> >> Is there anyway to get around this?
> >>
> >> Thanks,
> >> 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
> >> __________________________________________
> >>
> >>
> >> On 08.09.2011 10:48, Tirthankar Chakravarty wrote:
> >>> Use -gmm- and specify that you want the equations to be considered
> >>> independently (the moment conditions are independent). Note that the
> >>> point estimates are identical from two independent calls to -ivregress
> >>> 2sls- and the corresponding -gmm-. Throughout, "turn" is the included
> >>> endogenous variable.
> >>>
> >>> /**********************************************/
> >>> sysuse auto, clear
> >>> ivregress 2sls mpg gear_ratio (turn = weight length headroom)
> >>> ivregress 2sls mpg gear_ratio length (turn = weight length headroom)
> >>>
> >>> gmm (eq1: mpg - {b1}*turn - {b2}*gear_ratio - {b0}) ///
> >>>     (eq2: mpg - {c1}*turn - {c2}*gear_ratio -{c3}*length - {c0}), ///
> >>>     instruments(gear_ratio weight length headroom) ///
> >>>     onestep winitial(unadjusted, indep)
> >>> test [b2]_cons = [c2]_cons
> >>> /**********************************************/
> >>>
> >>> T
> >>>
> >>> On Thu, Sep 8, 2011 at 1:12 AM, Maarten Buis <maartenlbuis@gmail.com>
> >>> wrote:
> >>>> On Thu, Sep 8, 2011 at 9:56 AM, YUNHEE CHANG wrote:
> >>>>> I am estimating two differently-specified IV regressions and trying to
> >>>>> compare coefficients between the two models. I tried:
> >>>>>
> >>>>> ivregress 2sls y x1 x2 (x1=z)
> >>>>> est store reg1
> >>>>>
> >>>>> ivregress 2sls y x1 x2 x3 (x1=z)
> >>>>> est store reg2
> >>>>>
> >>>>> test [reg1]_b[x1]=[reg2]_b[x1]
> >>>>>
> >>>>> Then I get "equation [reg1] not found" error. What am I doing wrong?
> >>>> That might have worked after you combined both models with -suest-,
> >>>> but -ivregress- cannot be used together with -suest-. So what you want
> >>>> cannot be done.
> >>>>
> >>>> Sorry,
> >>>> Maarten
> >>>>
> >>>> --------------------------
> >>>> Maarten L. Buis
> >>>> Institut fuer Soziologie
> >>>> Universitaet Tuebingen
> >>>> Wilhelmstrasse 36
> >>>> 72074 Tuebingen
> >>>> Germany
> >>>>
> >>>>
> >>>> http://www.maartenbuis.nl
> >>>> --------------------------
> >>>> *
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> >>>>
> >>>
> >> *
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> >>
> >
> >
>
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