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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 08:10:47 -0700

Hi John,

That is because you have missed out a line continuation "///" in your
penultimate line, and you have misspelled the "onestep" option . Try
this:

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)

T


On Sat, Sep 17, 2011 at 8:02 AM, John Antonakis <John.Antonakis@unil.ch> 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
>>>>> --------------------------
>>>>> *
>>>>> *   For searches and help try:
>>>>> *   http://www.stata.com/help.cgi?search
>>>>> *   http://www.stata.com/support/statalist/faq
>>>>> *   http://www.ats.ucla.edu/stat/stata/
>>>>>
>>>>
>>> *
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>>>
>>
>>
>
> *
> *   For searches and help try:
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>



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

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