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Re: st: Comparing coefficients from two ivregress models


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
>>>> --------------------------
>>>> *
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>>>
>> *
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>
>

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