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Re: st: margeff and margeff at the mean differences


From   "Kelvin Foo" <kelvinski@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: margeff and margeff at the mean differences
Date   Wed, 14 Feb 2007 02:54:28 +0000

I should add that since the average marginal effect is the average of
the marginal effects over the observations in your sample, the points
away from the mean that are used in this calculation could have caused
the average marginal effect to be different from the marginal effect
at the mean.

On 2/14/07, Kelvin Foo <kelvinski@gmail.com> wrote:
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.
>
> Thank you in advance
> 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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