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Re: st: Problem with margins after logit on a person period data
From
Richard Williams <[email protected]>
To
[email protected], [email protected]
Subject
Re: st: Problem with margins after logit on a person period data
Date
Wed, 08 Jun 2011 22:52:40 -0500
The error message isn't terribly informative, but I believe the
problem is that mfx does not work with factor variables.
Also, if durat1-durat11 are mutually exclusive (i.e. you will have a
score of 1 on one of them and scores of 0 on everything else) then I
think you want something like i.durat. Otherwise the margins command
won't realize that the values of the variables depend on each other.
Also, if you want margins to produce the same results that mfx would
have produced, you should add the -atmeans- option (but many feel
that the default -asobserved- is better anyway).
At 09:31 PM 6/8/2011, Urmi Bhattacharya wrote:
Hi,
I dropped one of the duration dummies durat1 and ran the following
logit regression
logit school_left childage i.childfemale i.urban i.scstobc
i.casteother i.dadp i.dadm i.momp i.momm wagep wage5 wage8 wage9 distp
dist
> m disth percapcons durat2 durat3 durat4 durat5 durat6 durat7
durat8 durat9 durat10 durat11, nolog
I then used margins as follows
margins , dydx(_all)
I got results.
However, when i used the lame logit regreesion and then used mfx, I
got the following error message
default predict() is unsuitable for marginal-effect calculation
Why would i get error with mfx and not with margin?
Best
Urmi
On Wed, Jun 8, 2011 at 8:17 PM, Richard Williams
<[email protected]> wrote:
> I don't think margins likes it when you use the noconstant option. Is it
> really necessary for your model?
>
> At 05:08 PM 6/8/2011, Urmi Bhattacharya wrote:
>>
>> Dear Statalisters,
>>
>> I am running the following logit on a person-period data
>>
>> logit school_left childage i.childfemale i.urban i.scstobc
>> i.casteother i.dadp i.dadm i.momp i.momm wagep wage5 wage8 w
>> > age9 distp distm disth percapcons durat1 durat2 durat3 durat4 durat5
>> > durat6 durat7 durat8 durat9 durat10 durat11, nocon
>> > s nolog
>>
>> Logistic regression Number of obs =
>> 47569
>> Wald chi2(28) =
>> 14601.42
>> Log likelihood = -14502.393 Prob > chi2 =
>> 0.0000
>>
>>
>>
------------------------------------------------------------------------------
>> school_left | Coef. Std. Err. z P>|z| [95% Conf.
>> Interval]
>>
>>
-------------+----------------------------------------------------------------
>> childage | -.1413654 .006069 -23.29 0.000 -.1532605
>> -.1294703
>> 1.childfem~e | .0212387 .0306462 0.69 0.488 -.0388266
>> .0813041
>> 1.urban | .0124911 .0368072 0.34 0.734 -.0596497
>> .0846319
>> 1.scstobc | 1.972676 .1234074 15.99 0.000 1.730802
>> 2.21455
>> 1.casteother | 1.883233 .125722 14.98 0.000 1.636822
>> 2.129643
>> 1.dadp | .680585 .044327 15.35 0.000 .5937057
>> .7674643
>> 1.dadm | .3872552 .0506982 7.64 0.000 .2878886
>> .4866217
>> 1.momp | 1.351053 .0835161 16.18 0.000 1.187364
>> 1.514741
>> 1.momm | .9494066 .0918146 10.34 0.000 .7694532
>> 1.12936
>> wagep | -.0281962 .0061845 -4.56 0.000 -.0403176
>> -.0160748
>> wage5 | -.0040992 .0045604 -0.90 0.369 -.0130373
>> .004839
>> wage8 | .021027 .0043869 4.79 0.000 .0124289
>> .0296251
>> wage9 | .0159427 .0022335 7.14 0.000 .0115652
>> .0203203
>> distp | -.0355589 .0162329 -2.19 0.028 -.0673748
>> -.0037431
>> distm | -.0024083 .0112658 -0.21 0.831 -.0244888
>> .0196722
>> disth | .0186932 .0039384 4.75 0.000 .010974
>> .0264124
>> percapcons | -.0001459 .0000278 -5.26 0.000 -.0002003
>> -.0000915
>> durat1 | -5.771466 .1406919 -41.02 0.000 -6.047217
>> -5.495715
>> durat2 | -4.947835 .1222363 -40.48 0.000 -5.187414
>> -4.708256
>> durat3 | -4.690019 .1193602 -39.29 0.000 -4.923961
>> -4.456078
>> durat4 | -4.054464 .1132586 -35.80 0.000 -4.276447
>> -3.832481
>> durat5 | -3.055883 .1082449 -28.23 0.000 -3.268039
>> -2.843727
>> durat6 | -3.560284 .1130896 -31.48 0.000 -3.781936
>> -3.338633
>> durat7 | -2.825943 .1099477 -25.70 0.000 -3.041436
>> -2.610449
>> durat8 | -2.238741 .1094063 -20.46 0.000 -2.453173
>> -2.024308
>> durat9 | -1.427979 .1099217 -12.99 0.000 -1.643421
>> -1.212536
>> durat10 | -.3967904 .1152271 -3.44 0.001 -.6226313
>> -.1709495
>> durat11 | -2.168164 .1486318 -14.59 0.000 -2.459477
>> -1.876851
>>
>>
------------------------------------------------------------------------------
>>
>> .
>> end of do-file
>>
>> Since I am interested in the marginal effects of the variables on the
>> probability of hazard,
>>
>> I do
>>
>> margins,dydx(*)
>>
>> But this gives me the following output
>>
>> margins,dydx(*)
>>
>> Average marginal effects Number of obs =
>> 47569
>> Model VCE : OIM
>>
>> Expression : Pr(school_left), predict()
>> dy/dx w.r.t. : childage 1.childfemale 1.urban 1.scstobc 1.casteother
>> 1.dadp 1.dadm 1.momp 1.momm wagep wage5 wage8
>> wage9 distp distm disth percapcons durat1 durat2 durat3
>> durat4 durat5 durat6 durat7 durat8 durat9
>> durat10 durat11
>>
>>
>>
------------------------------------------------------------------------------
>> | Delta-method
>> | dy/dx Std. Err. z P>|z| [95% Conf.
>> Interval]
>>
>>
-------------+----------------------------------------------------------------
>> childage | (not estimable)
>> 1.childfem~e | (not estimable)
>> 1.urban | (not estimable)
>> 1.scstobc | (not estimable)
>> 1.casteother | (not estimable)
>> 1.dadp | (not estimable)
>> 1.dadm | (not estimable)
>> 1.momp | (not estimable)
>> 1.momm | (not estimable)
>> wagep | (not estimable)
>> wage5 | (not estimable)
>> wage8 | (not estimable)
>> wage9 | (not estimable)
>> distp | (not estimable)
>> distm | (not estimable)
>> disth | (not estimable)
>> percapcons | (not estimable)
>> durat1 | (not estimable)
>> durat2 | (not estimable)
>> durat3 | (not estimable)
>> durat4 | (not estimable)
>> durat5 | (not estimable)
>> durat6 | (not estimable)
>> durat7 | (not estimable)
>> durat8 | (not estimable)
>> durat9 | (not estimable)
>> durat10 | (not estimable)
>> durat11 | (not estimable)
>>
>>
------------------------------------------------------------------------------
>> Note: dy/dx for factor levels is the discrete change from the base level.
>>
>> Can someone explain what am I doing wrong? How do I get the marginal
>> effects after running the logit?
>>
>> Best
>>
>> Urmi
>> *
>> * 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/
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME: (574)289-5227
> EMAIL: [email protected]
> WWW: http://www.nd.edu/~rwilliam
>
> *
> * 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/
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
*
* 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/