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# Re: st: IVPROBIT and marginal effects

 From Shikha Sinha To statalist@hsphsun2.harvard.edu Subject Re: st: IVPROBIT and marginal effects Date Wed, 8 Aug 2012 18:49:42 -0700

```For example, below I report the outputs from -ivreg2, -ivprobit,
margins, and margins with predict(pr)

-ivprobit coeff is exactly similar to margins,dydx(dist_min), however,
margins with predicted prob is different.

My question is which coeff (-0.0273112 vs  -.0774099)  is comparable
to -ivreg2 coeff of  -0.02773.

Thanks,
Shikha

ivreg2 ifd \$transport (dist_min=distindex)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

Number of obs =   163659
F(  3,163655) =  1474.76
Prob > F      =   0.0000
Total (centered) SS     =  37846.80961                Centered R2   =  -0.0220
Total (uncentered) SS   =        59422                Uncentered R2 =   0.3491
Residual SS             =  38677.86458                Root MSE      =    .4861

------------------------------------------------------------------------------
ifd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dist_min |    -.02773   .0006473   -42.84   0.000    -.0289987   -.0264612
allwtd |   .0521262   .0036099    14.44   0.000     .0450509    .0592015
carmoto |   .3531489   .0100414    35.17   0.000     .3334681    .3728297
_cons |   .4503572   .0053071    84.86   0.000     .4399556    .4607589
------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):         1.5e+04
Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              1.7e+04
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
15% maximal IV size              8.96
20% maximal IV size              6.66
25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
(equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         dist_min
Included instruments: allwtd carmoto
Excluded instruments: distindex
------------------------------------------------------------------------------

. ivprobit ifd \$transport (dist_min=distindex), nolog asis

Probit model with endogenous regressors           Number of obs   =     163659
Wald chi2(3)    =    5631.07
Log likelihood = -622595.14                       Prob > chi2     =     0.0000

------------------------------------------------------------------------------
|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dist_min |  -.0774099   .0015262   -50.72   0.000    -.0804012   -.0744186
allwtd |   .1422951   .0098945    14.38   0.000     .1229024    .1616879
carmoto |   .8970391   .0280716    31.96   0.000     .8420199    .9520583
_cons |  -.0949859    .014482    -6.56   0.000    -.1233702   -.0666016
-------------+----------------------------------------------------------------
/athrho |    .329658   .0105356    31.29   0.000     .3090087    .3503074
/lnsigma |   1.748964   .0017479  1000.61   0.000     1.745538    1.752389
-------------+----------------------------------------------------------------
rho |   .3182135   .0094687                       .299535    .3366481
sigma |   5.748641    .010048                      5.728981    5.768369
------------------------------------------------------------------------------
Instrumented:  dist_min
Instruments:   allwtd carmoto distindex
------------------------------------------------------------------------------
Wald test of exogeneity (/athrho = 0): chi2(1) =   979.06 Prob > chi2 = 0.0000

. margins, dydx(dist_min) pred(pr)

Average marginal effects                          Number of obs   =     163659
Model VCE    : OIM

Expression   : Probability of positive outcome, predict(pr)
dy/dx w.r.t. : dist_min

------------------------------------------------------------------------------
|            Delta-method
|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dist_min |  -.0273112   .0005023   -54.37   0.000    -.0282957   -.0263266
------------------------------------------------------------------------------

. margins, dydx(dist_min)

Average marginal effects                          Number of obs   =     163659
Model VCE    : OIM

Expression   : Fitted values, predict()
dy/dx w.r.t. : dist_min

------------------------------------------------------------------------------
|            Delta-method
|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dist_min |  -.0774099   .0015262   -50.72   0.000    -.0804012   -.0744186
------------------------------------------------------------------------------

On Wed, Aug 8, 2012 at 6:09 PM, Shikha Sinha <shikha.sinha414@gmail.com> wrote:
> Hi all,
>
> Are -ivprobit coefficients marginal effects?
>
> or will have to use -margins, dydx(_all) after -ivprobit to estimate
> the marginal effect?
>
> Thanks,
> Shikha
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```