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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 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 = 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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