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Re: st: Comparing coefficients from two ivregress models
From
Tirthankar Chakravarty <[email protected]>
To
[email protected]
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 <[email protected]> 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 <[email protected]>
>> 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 <[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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>>>>> * http://www.ats.ucla.edu/stat/stata/
>>>>>
>>>>
>>> *
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>>
>>
>
> *
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>
--
Tirthankar Chakravarty
[email protected]
[email protected]
*
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