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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 17:02:45 +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
<[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
>>>> --------------------------
>>>> *
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>>>>
>>>
>> *
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>
>
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