Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: st: Comparing coefficients from two ivregress models
From
Tirthankar Chakravarty <[email protected]>
To
[email protected]
Subject
Re: st: Comparing coefficients from two ivregress models
Date
Sat, 17 Sep 2011 07:57:15 -0700
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/
>
--
Tirthankar Chakravarty
[email protected]
[email protected]
*
* 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/