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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 08:13:17 -0700

John, as in the code I sent, you need to add another line continuation
"///" at the end of your penultimate line.

T

On Sat, Sep 17, 2011 at 8:09 AM, John Antonakis <John.Antonakis@unil.ch> wrote:
> Pardon the typo.....it was "onestep" that I had written (and not "onstep").
> Thus the syntax was:
>
> 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)
>    onestep winitial(unadjusted, indep) vce(unadjusted)
>
> The error still remains.
>
> 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 17:02, John Antonakis wrote:
>> 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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-- 
Tirthankar Chakravarty
tchakravarty@ucsd.edu
tirthankar.chakravarty@gmail.com

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