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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 17:02:45 +0200 |
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 >>>> -------------------------- >>>> * >>>> * 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: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/