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Re: st: Comparing coefficients from two ivregress models
From
John Antonakis <[email protected]>
To
[email protected]
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
<[email protected]> 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 <[email protected]>
> >>> 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
> >>>> --------------------------
> >>>> *
> >>>> * 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:
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