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Re: st: Different results of mlogit on different machine


From   Stas Kolenikov <[email protected]>
To   [email protected]
Subject   Re: st: Different results of mlogit on different machine
Date   Thu, 9 Feb 2006 13:12:09 -0600

You have -robust- option in one of your runs, but not in the other.
This affects how the estimate of the VCE is computed, and I believe
this would also affect the running estimate of the Hessian. The
initial steps before iteration 0 are performed without regard to the
curvature of the likelihood being maximized, so the two procedures
start from the same point. But then they are using somewhat different
estimates of the second derivative matrix, I guess, and that's where
the discrepancies are coming out of. By the way, neither of the
estimation procedures converged. With 40 observations, there is no way
you get reasonable results for 28 parameters. No way. And you do see
that in some parameters shooting off to infinity.

On 2/9/06, Qiuqiong Huang <[email protected]> wrote:
> Hi
> I found that when I run mlogit on different machine, I got different
> results. The data and variables used in the regression are exactly the
> same. But one machine ran 13 iterations.  another machine only ran 11
> iterations and did not converge. And the results (magnitudes and standard
> errors of coefficients are totally different too). On both machine, I
> used Stata 8.2.
>
> is there any way I can find out why i got different results?   Below are
> the two sets of results.
>
> Thanks a lot.
> Qiuqiong
> ***********************************************************************
> ***********************************************************************
> ***********************************************************************
> Machine 1:
> . xi: mlogit incentive2 DClined seri_mud avgDCnumgate_2 avghhplot wstress
> age_vill edu_vill pthsch ptnonag
> >  ptmig i.county, b(1) robust tr
> i.county          _Icounty_31-35      (naturally coded; _Icounty_31
> omitted)
>
> Iteration 0:   log pseudolikelihood = -41.286362
>                      0          0          0          0          0
>                      0          0          0          0          0
>                      0          0          0          0  -.9444616
>                      0          0          0          0          0
>                      0          0          0          0          0
>                      0          0          0          0  -.1823216
>
> Iteration 1:   log pseudolikelihood = -23.942025
>              -.1535105  -1.862865  -.0126249  -.1058805  -.0903134
>              -.0083592   .0718334   .0156798  -.0209434  -.0644558
>               3.273693   .7684405   1.147216  -.7303947   1.996602
>               .7346775   .4799768  -.0084356   .3432856   .8750169
>               .0167751  -.1539001   .0164632  -.0378387   .0005579
>              -2.041441  -.3045206  -.4144813   -1.28777  -4.461722
>
> Iteration 2:   log pseudolikelihood =  -20.07641
> .......Omitted here
>
> Iteration 10:  log pseudolikelihood = -4.2511694
>               216.9474  -83.40591  -.0279035   6.761672   64.32783
>               22.57524    27.4512   8.661309  -10.73628  -3.635502
>               222.3915   41.19815   30.15323  -13.08641  -1635.229
>               200.3627  -3.597955   .0447571   41.53482   87.61121
>               15.90677    27.2541   8.738907  -8.765639  -.4245701
>              -83.01943  -71.07202  -171.0882  -218.1327  -1636.855
>
> Iteration 11:  log pseudolikelihood = -3.7138854
>               264.8277  -99.28156  -.0282477     7.7863   81.29004
>               27.57892   32.82099   10.73473  -13.09572  -4.473515
>               267.4728   44.75714   35.35282  -20.16417   -2002.28
>               246.2304  -4.792918   .0514819    51.2159   110.3305
>               19.22811   32.69608   10.69847  -10.85353  -.3073647
>              -110.3294  -89.29089  -215.4751  -269.2497  -1999.169
>
> Iteration 12:  log pseudolikelihood = -3.5259999
>               290.1185  -106.7525  -.0283225   9.239672   89.29666
>               30.11146   35.97972   11.79514  -14.29023  -4.862053
>               286.5799   45.57917   34.45153  -27.19174  -2195.181
>               270.1947  -5.378952   .0567416   56.18665   121.0073
>               21.10159   35.89417   11.73846  -11.91466  -.3277298
>               -120.984  -97.89167  -236.4222  -295.3328  -2193.674
>
> Iteration 13:  log pseudolikelihood =  -3.510283
>               296.3671  -108.5992  -.0283358   9.596246   91.27622
>               30.73784   36.75933   12.05715  -14.58576  -4.958122
>               291.3211   45.79315   34.24537  -28.90659  -2242.855
>               276.1129  -5.525269   .0580142   57.41468   123.6471
>               21.56427   36.68407   11.99532  -12.17688  -.3327286
>              -123.6217  -100.0199   -241.603  -301.7788  -2241.715
>
>
> Multinomial logistic regression                   Number of obs   =
> 40
>                                                    Wald chi2(24)   =
> .
>                                                    Prob > chi2     =
> .
> Log pseudolikelihood = -3.5107012                 Pseudo R2       =
> 0.9150
> ***********************************************************************
> ***********************************************************************
> ***********************************************************************
>
>
> Machine 2:
>
>
> . xi: mlogit incentive2 DClined seri_mud avgDCnumgate_2 avghhplot wstress
> age_vill edu_vill pthsch ptnonag
> > ptmig i.county, b(1) tr
> i.county          _Icounty_31-35      (naturally coded; _Icounty_31
> omitted)
>
> Iteration 0:   log likelihood = -41.286362
>                      0          0          0          0          0
>                      0          0          0          0          0
>                      0          0          0          0  -.9444616
>                      0          0          0          0          0
>                      0          0          0          0          0
>                      0          0          0          0  -.1823216
>
> Iteration 1:   log likelihood = -23.942025
>              -.1535105  -1.862865  -.0126249  -.1058805  -.0903134
>              -.0083592   .0718334   .0156798  -.0209434  -.0644558
>               3.273693   .7684405   1.147216  -.7303947   1.996602
>               .7346775   .4799768  -.0084356   .3432856   .8750169
>               .0167751  -.1539001   .0164632  -.0378387   .0005579
>              -2.041441  -.3045206  -.4144813   -1.28777  -4.461722
>
> Iteration 2:   log likelihood =  -20.07641
>               .7064876  -2.962282  -.0142221  -.1936033     .08562
>   ........Omitted.......
>
> Iteration 10:  log likelihood = -4.2511694
>               216.9474  -83.40591  -.0279035   6.761672   64.32783
>               22.57524    27.4512   8.661309  -10.73628  -3.635502
>               222.3915   41.19815   30.15323  -13.08641  -1635.229
>               200.3627  -3.597955   .0447571   41.53482   87.61121
>               15.90677    27.2541   8.738907  -8.765639  -.4245701
>              -83.01943  -71.07202  -171.0882  -218.1327  -1636.855
>
> Iteration 11:  log likelihood =          .
>               312.7081  -115.1572  -.0285919   8.810928   98.25225
>                32.5826   38.19077   12.80816  -15.45516  -5.311528
>                312.554   48.31613    40.5524  -27.24193  -2369.331
>                292.098  -5.987881   .0582066   60.89697   133.0497
>               22.54945   38.13805   12.65803  -12.94142  -.1901592
>              -137.6394  -107.5098   -259.862  -320.3667  -2361.483
>
>
> Multinomial logistic regression                   Number of obs   =
> 40
>                                                    LR chi2(28)     =
> .
> Log likelihood =          .                       Prob > chi2     =
> .
> *
> *   For searches and help try:
> *   http://www.stata.com/support/faqs/res/findit.html
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>


--
Stas Kolenikov
http://stas.kolenikov.name

*
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