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Re: st: mlogit and outreg after mlogit


From   "Joao Ricardo F. Lima" <jricardofl@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: mlogit and outreg after mlogit
Date   Mon, 28 Jul 2008 11:48:35 -0300

Dear Dilek,

for me, works fine, the same coeffs, se and significance levels:

***begin****************************************
webuse sysdsn3
mlogit insure age male nonwhite site2 site3
mlogit insure nonwhite site2 site3 age male
***end******************************************

Is your outreg updated in desktop and laptop?

. which outreg
c:\ado\plus\o\outreg.ado
*! version 3.1.8  16mar07  by jgallup@cal.berkeley.edu
*! Write formatted regression output to a text file

. mlogit insure age male nonwhite site2 site3

Iteration 0:   log likelihood = -555.85446
Iteration 1:   log likelihood = -534.72983
Iteration 2:   log likelihood = -534.36536
Iteration 3:   log likelihood = -534.36165
Iteration 4:   log likelihood = -534.36165

Multinomial logistic regression                   Number of obs   =        615
                                                  LR chi2(10)     =      42.99
                                                  Prob > chi2     =     0.0000
Log likelihood = -534.36165                       Pseudo R2       =     0.0387

------------------------------------------------------------------------------
      insure |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Prepaid      |
         age |   -.011745   .0061946    -1.90   0.058    -.0238862    .0003962
        male |   .5616934   .2027465     2.77   0.006     .1643175    .9590693
    nonwhite |   .9747768   .2363213     4.12   0.000     .5115955    1.437958
       site2 |   .1130359   .2101903     0.54   0.591    -.2989296    .5250013
       site3 |  -.5879879   .2279351    -2.58   0.010    -1.034733   -.1412433
       _cons |   .2697127   .3284422     0.82   0.412    -.3740222    .9134476
-------------+----------------------------------------------------------------
Uninsure     |
         age |  -.0077961   .0114418    -0.68   0.496    -.0302217    .0146294
        male |   .4518496   .3674867     1.23   0.219     -.268411     1.17211
    nonwhite |   .2170589   .4256361     0.51   0.610    -.6171725     1.05129
       site2 |  -1.211563   .4705127    -2.57   0.010    -2.133751   -.2893747
       site3 |  -.2078123   .3662926    -0.57   0.570    -.9257327     .510108
       _cons |  -1.286943   .5923219    -2.17   0.030    -2.447872   -.1260135
------------------------------------------------------------------------------
(insure==Indemnity is the base outcome)


. mlogit insure nonwhite site2 site3 age male

Iteration 0:   log likelihood = -555.85446
Iteration 1:   log likelihood = -534.72983
Iteration 2:   log likelihood = -534.36536
Iteration 3:   log likelihood = -534.36165
Iteration 4:   log likelihood = -534.36165

Multinomial logistic regression                   Number of obs   =        615
                                                  LR chi2(10)     =      42.99
                                                  Prob > chi2     =     0.0000
Log likelihood = -534.36165                       Pseudo R2       =     0.0387

------------------------------------------------------------------------------
      insure |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Prepaid      |
    nonwhite |   .9747768   .2363213     4.12   0.000     .5115955    1.437958
       site2 |   .1130359   .2101903     0.54   0.591    -.2989296    .5250013
       site3 |  -.5879879   .2279351    -2.58   0.010    -1.034733   -.1412433
         age |   -.011745   .0061946    -1.90   0.058    -.0238862    .0003962
        male |   .5616934   .2027465     2.77   0.006     .1643175    .9590693
       _cons |   .2697127   .3284422     0.82   0.412    -.3740222    .9134476
-------------+----------------------------------------------------------------
Uninsure     |
    nonwhite |   .2170589   .4256361     0.51   0.610    -.6171725     1.05129
       site2 |  -1.211563   .4705127    -2.57   0.010    -2.133751   -.2893747
       site3 |  -.2078123   .3662926    -0.57   0.570    -.9257327     .510108
         age |  -.0077961   .0114418    -0.68   0.496    -.0302217    .0146294
        male |   .4518496   .3674867     1.23   0.219     -.268411     1.17211
       _cons |  -1.286943   .5923219    -2.17   0.030    -2.447872   -.1260135
------------------------------------------------------------------------------
(insure==Indemnity is the base outcome)



2008/7/28 Dilek Cetin <dcetin@metu.edu.tr>:
> Dear statalisters
>
> I have two problems with multinomial logit estimation.
>
> 1. I run the first regression, mlogit y x1 x2 x3 x4 x5 x6 x7 .... x20
> then I run the second regressin which is mlogit y x1 x2 x3 x7 ... x20 x4 x5
> zx6
> y=1, ...,5
>
> In the second regression the coefficents, standart errors and significance
> levels are totally different.
>
> Even if I have tried the robust estimation, the problem still exists.
>
> In the multinomial logit estimation why the order of the independents are
> matter???
>
> 2. When I used the outreg command after mlogit in my desktop, I have 4
> categories in the output file (y=1, ...,5 - outcome(1) is the base outcome).
> When I used this command in my laptop, I have only one category in the
> output file. How can I solve this problem?
>
> All the best
> Thanks
>
> Dilek
>
>
> *
> *   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/
>



-- 
-------------------------------
Joao Ricardo Lima
Professor
UFPB-CCA-DCFS
+553138923914
-------------------------------
*
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