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Re: st: Comparing coefficients from two ivregress models


From   Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com>
To   statalist@hsphsun2.harvard.edu
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 <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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>



-- 
Tirthankar Chakravarty
tchakravarty@ucsd.edu
tirthankar.chakravarty@gmail.com

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