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From |
John Antonakis <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Comparing coefficients from two ivregress models |

Date |
Sat, 17 Sep 2011 17:20:06 +0200 |

Shoot! Did not notice my mistake! Thank you so much Tirthankar! Your solution with GMM is so simple and elegant.

. test [b1]_cons = [c1]_cons = [d1]_cons ( 1) [b1]_cons - [c1]_cons = 0 ( 2) [b1]_cons - [d1]_cons = 0 chi2( 2) = 0.66 Prob > chi2 = 0.7179 Works like a charm.

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 17:13, Tirthankar Chakravarty wrote: > John, as in the code I sent, you need to add another line continuation > "///" at the end of your penultimate line. > > T >

>> Thus the syntax was: >> >> gmm (eq1: turnover - {b1}*lmx - {b0}) ///

>> (eq3: turnover - {d1}*lmx - {d2}*l_incentives - {d3}*l_iq -

>> 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}) ///

>>> /// >>> (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) >>>

>>> the equation 3 instruments): >>> >>> . gmm (eq1: turnover - {b1}*lmx - {b0}) ///

>>>> /// >>>> (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(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()

>>> 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 >>>>

>>>> wrote: >>>>> Hi: >>>>>

>>>>> three >>>>> equations that I would like to "stack" and then make cross-equations >>>>> tests.

>>>>> 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

>>>>> 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

>>>>> /// >>>>>> 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

>>>>> 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

>>>>> 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 >>>>> >>>>>

>>>>> 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

>>>>>> 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}) ///

>>>>>> instruments(gear_ratio weight length headroom) /// >>>>>> onestep winitial(unadjusted, indep) >>>>>> test [b2]_cons = [c2]_cons >>>>>> /**********************************************/ >>>>>> >>>>>> T >>>>>>

>>>>>> wrote: >>>>>>> On Thu, Sep 8, 2011 at 9:56 AM, YUNHEE CHANG wrote:

>>>>>>>> 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] >>>>>>>>

>>>>>>> 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/ >>>>> >>>> >>> * >>> * 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/ >> > > * * 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/

**References**:**st: Comparing coefficients from two ivregress models***From:*YUNHEE CHANG <[email protected]>

**Re: st: Comparing coefficients from two ivregress models***From:*Maarten Buis <[email protected]>

**Re: st: Comparing coefficients from two ivregress models***From:*Tirthankar Chakravarty <[email protected]>

**Re: st: Comparing coefficients from two ivregress models***From:*John Antonakis <[email protected]>

**Re: st: Comparing coefficients from two ivregress models***From:*Tirthankar Chakravarty <[email protected]>

**Re: st: Comparing coefficients from two ivregress models***From:*John Antonakis <[email protected]>

**Re: st: Comparing coefficients from two ivregress models***From:*John Antonakis <[email protected]>

**Re: st: Comparing coefficients from two ivregress models***From:*Tirthankar Chakravarty <[email protected]>

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