# Re: st: margeff and margeff at the mean differences

 From "Kelvin Foo" To statalist@hsphsun2.harvard.edu Subject Re: st: margeff and margeff at the mean differences Date Wed, 14 Feb 2007 02:14:36 +0000

```Hi,

I am not familiar with -margeff- but I believe your query is answered
in the following link, regarding -mfx-:
http://www.stata.com/statalist/archive/2004-03/msg00142.html

There might be other similar discussions in the Statalist archives/FAQs.

Hope this helps,
Kelvin.

On 2/14/07, Nikos Nikiforakis <n.nikiforakis@unimelb.edu.au> wrote:
```
```Dear members of Statalist,

I am estimating the average marginal effects following a probit with
random effects using -margeff-. My 'problem' is that the average
marginal effect of one variable (x2) turns out to be not significant,
although it is significant at the 1-percent level in the probit and
also when I use -margeff- to estimate the marginal effects at the
mean. (The marginal effects of all other variables are very similar).

Any idea why this might be happening. I would be grateful for any
suggestions as I could not find an answer in Bartus (2005, SJ) which
introduces -margeff- or elsewhere.

Nikos

Here is the Stata output (x1 x3 and x4 are dummy variables)

. xtprobit y x1 x2 x3 x4 x5, i(cluster)

Fitting comparison model:

Iteration 0:   log likelihood =  -2759.006
Iteration 1:   log likelihood = -2267.4507
Iteration 2:   log likelihood =  -2257.582
Iteration 3:   log likelihood = -2257.5472

Fitting full model:

rho =  0.0     log likelihood = -2257.5472
rho =  0.1     log likelihood = -2196.2157
rho =  0.2     log likelihood =  -2201.078

Iteration 0:   log likelihood = -2195.3866
Iteration 1:   log likelihood = -2191.6554
Iteration 2:   log likelihood = -2191.6139
Iteration 3:   log likelihood = -2191.6139

Random-effects probit regression                Number of obs      =      5760
Group variable (i): cluster                     Number of groups   =        32

Random effects u_i ~ Gaussian                   Obs per group: min =       120
avg =     180.0
max =       360

Wald chi2(5)       =    775.44
Log likelihood  = -2191.6139                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 |  -.7448295   .1461338    -5.10   0.000    -1.031246   -.4584126
x2 |   .1511212   .0059109    25.57   0.000      .139536    .1627064
x3 |  -.0558861   .1622825    -0.34   0.731     -.373954    .2621818
x4 |  -.0024064   .1447156    -0.02   0.987    -.2860437     .281231
x5 |  -.0769059   .0076225   -10.09   0.000    -.0918457   -.0619662
_cons |  -.5891004   .1766414    -3.34   0.001    -.9353111   -.2428897
-------------+----------------------------------------------------------------
/lnsig2u |   -1.93502    .322295                     -2.566707   -1.303333
-------------+----------------------------------------------------------------
sigma_u |   .3800282   .0612406                      .2771065    .5211765
rho |    .126196   .0355397                      .0713121    .2136046
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =   131.87 Prob >= chibar2 = 0.000

. margeff

Average partial effects after xtprobit
y  = Pr(y)

------------------------------------------------------------------------------
variable |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 |  -.1497686    .022374    -6.69   0.000    -.1936208   -.1059164
x2 |   .0007352    .001196     0.61   0.539    -.0016089    .0030793
x3 |  -.0113159   .0324428    -0.35   0.727    -.0749025    .0522707
x4 |  -.0004869   .0292553    -0.02   0.987    -.0578262    .0568523
x5 |  -.0155627   .0017951    -8.67   0.000     -.019081   -.0120444
------------------------------------------------------------------------------

. margeff, at(mean)

Partial effects at fixed values after xtprobit
y  = Pr(y)

------------------------------------------------------------------------------
variable |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 |  -.1617599   .0343461    -4.71   0.000    -.2290771   -.0944427
x2 |   .0326642   .0031176    10.48   0.000     .0265538    .0387747
x3 |  -.0120799   .0355729    -0.34   0.734    -.0818015    .0576417
x4 |  -.0005201   .0312798    -0.02   0.987    -.0618274    .0607872
x5 |  -.0166266   .0021715    -7.66   0.000    -.0208826   -.0123706
------------------------------------------------------------------------------

Nikos Nikiforakis
Research Fellow
Dept. Economics
The University of Melbourne
http://www.economics.unimelb.edu.au/staffprofile/nnikiforakis.htm

Nikos Nikiforakis
Dept. Economics
The University of Melbourne
http://www.economics.unimelb.edu.au/staffprofile/nnikiforakis.htm

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```
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```