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| 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 16:02:34 +0200 |
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 = 8Initial 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 >> -------------------------- >> * >> * 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/