Hmmmm!
I appreciate your time guys.
Here are my commands and results.
. ivprobit employment ( OBESEbmi=diet1 diet2) centage centagesq
edu2 edu3 edu4 edu5 edu6 marriage2 marri
> age3 marriage4 children
Fitting exogenous probit model
Iteration 0: log likelihood = -597.05914
Iteration 1: log likelihood = -415.87674
Iteration 2: log likelihood = -403.33373
Iteration 3: log likelihood = -402.89898
Iteration 4: log likelihood = -402.89762
Iteration 5: log likelihood = -402.89762
Fitting full model
Iteration 0: log likelihood = -746.36499
Iteration 1: log likelihood = -744.94761
Iteration 2: log likelihood = -744.38178
Iteration 3: log likelihood = -744.38
Iteration 4: log likelihood = -744.38
Probit model with endogenous regressors Number of obs = 1045
Wald chi2(12) = 281.90
Log likelihood = -744.38 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
employment |
OBESEbmi | -.8835098 .4013275 -2.20 0.028 -1.670097 -.0969223
centage | -.0114935 .0039625 -2.90 0.004 -.0192598 -.0037272
centagesq | -.0026056 .0002318 -11.24 0.000 -.0030599 -.0021513
edu2 | -.5586423 .1567106 -3.56 0.000 -.8657895 -.2514951
edu3 | .152094 .1354019 1.12 0.261 -.1132888 .4174769
edu4 | .4574337 .1725559 2.65 0.008 .1192304 .795637
edu5 | .4399531 .1929985 2.28 0.023 .0616829 .8182233
edu6 | .9912227 .3626966 2.73 0.006 .2803504 1.702095
marriage2 | -.0014036 .181648 -0.01 0.994 -.3574272 .3546199
marriage3 | -.4220406 .1645533 -2.56 0.010 -.7445591 -.099522
marriage4 | -.6121703 .2256473 -2.71 0.007 -1.054431 -.1699096
children | -.0192385 .0425978 -0.45 0.652 -.1027286 .0642516
_cons | 1.534364 .1681024 9.13 0.000 1.20489 1.863839
-------------+----------------------------------------------------------------
/lnsigma | -1.090328 .0218741 -49.85 0.000 -1.133201 -1.047456
/athrho | .3088546 .1551784 1.99 0.047 .0047107 .6129986
-------------+----------------------------------------------------------------
sigma | .3361062 .007352 .322001 .3508292
rho | .2993948 .1412686 .0047106 .5462345
------------------------------------------------------------------------------
Instrumented: OBESEbmi
Instruments: centage centagesq edu2 edu3 edu4 edu5 edu6 marriage2 marriage3
marriage4 children diet1 diet2
------------------------------------------------------------------------------
Wald test of exogeneity (/athrho = 0): chi2(1) = 3.96 Prob > chi2 = 0.0466
. mfx
warning: predict() expression unsuitable for standard-error calculation;
option nose imposed
Marginal effects after ivprobit
y = Fitted values (predict)
= .77898262
-------------------------------------------------------------------------------
variable | dy/dx X
---------------------------------+---------------------------------------------
OBESEbmi | -.8835098 .159183
centage | -.0114935 -.21666
centagesq | -.0026056 251.145
edu2 | -.5586423 .125493
edu3 | .152094 .228086
edu4 | .4574337 .159307
edu5 | .4399531 .144449
edu6 | .9912227 .056753
marriage2 | -.0014036 .099244
marriage3 | -.4220406 .085597
marriage4 | -.6121703 .061284
children | -.0192385 2.40813
diet1 | 0 .613461
diet2 | 0 .150254
-------------------------------------------------------------------------------
On 9/13/05, Scott Merryman <[email protected]> wrote:
>
> > -----Original Message-----
> > From: [email protected] [mailto:owner-
> > [email protected]] On Behalf Of Tinna
> > Sent: Tuesday, September 13, 2005 3:37 PM
> > To: [email protected]
> > Subject: Re: st: RE: Marginal effects after ivprobit
> >
> > Thanks for the answer Scott. Yes I am pretty sure.
> >
> > If you try the same estimations again without regressing quietly then
> > you will probably see that the coefficients you get after mfx are the
> > same as from the original estimation.
>
> <snip>
>
> But they are not.
>
> . webuse laborsup, clear
>
> . ivprobit fem_work fem_educ kids (other_inc = male_educ) , nolog
>
> Probit model with endogenous regressors Number of obs = 500
> Wald chi2(3) = 163.88
> Log likelihood = -2368.2062 Prob > chi2 = 0.0000
>
> ----------------------------------------------------------------------------
> Coef. Std. Err. z P>|z| [95% Conf. Interval]
> -------------+--------------------------------------------------------------
> fem_work |
> other_inc | -.0542756 .0060854 -8.92 0.000 -.0662027 -.04234
> fem_educ | .211111 .0268648 7.86 0.000 .1584569 .26376
> kids | -.1820929 .0478267 -3.81 0.000 -.2758316 -.08835
> _cons | .3672083 .4480724 0.82 0.412 -.5109975 1.2454
> -------------+--------------------------------------------------------------
> /lnsigma | 2.813383 .0316228 88.97 0.000 2.751404 2.8753
> /athrho | .3907858 .1509443 2.59 0.010 .0949403 .68663
> -------------+--------------------------------------------------------------
> sigma | 16.66621 .5270318 15.66461 17.731
> rho | .3720374 .1300519 .0946561 .59581
> ----------------------------------------------------------------------------
> Instrumented: other_inc
> Instruments: fem_educ kids male_educ
> ----------------------------------------------------------------------------
> Wald test of exogeneity (/athrho = 0): chi2(1) = 6.70 Prob > chi2 =
> 0.0096
>
> . estimates store iv
>
> . mfx, predict(p)
>
> warning: predict() expression p unsuitable for standard-error calculation;
> option nose imposed
>
>
> Marginal effects after ivprobit
> y = Probability of positive outcome (predict, p)
> = .44363395
> ----------------------------------------------------------------------------
> variable | dy/dx X
> ---------------------------------+------------------------------------------
> other_inc | -.0214364 49.6023
> fem_educ | .0833791 12.046
> kids | -.0719183 1.976
> male_educ | 0 11.966
> ----------------------------------------------------------------------------
>
> . estimates store mfx
>
> Or, all together for easy comparison:
>
> . estout iv mfx, style(fixed) margin meqs(fem_work) label varwidth(24)
> varlabels(_cons Constant) keep(fem_work:) collabels(,none)
>
> iv mfx
> Does female work?
> Other income -.0542756 -.0214364
> Female education level .211111 .0833791
> Number of children -.1820929 -.0719183
> Constant
>
>
> Scott
>
>
>
>
> *
> * 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/
>
*
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