Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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 16:02:34 +0200

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/
>>
>
>

*
*   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/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index