# Re: st: Marginal effects

 From "Joao Ricardo F. Lima" <[email protected]> To [email protected] Subject Re: st: Marginal effects Date Sun, 20 Apr 2008 03:18:23 -0300

```Maarten and Statalisters,

I'm having this same problem to calculate marginal effects but using mlogit.
First I don't know how to calculate the xb only to the outcome==1, for example.
Second I can't use normalden and I don't find the density function
logistic in Stata.

> #delimit ;
> local xb "_b[_cons] +
>          _b[ttl_exp]*`mttl' +
>
>
> nlcom normalden(`xb') *
> #delimit cr

Thank you,

Joao Ricardo

2008/3/22, Maarten buis <[email protected]>:
> --- Jos� C. <[email protected]> wrote:
> >   Maarten specifically thinking about the case of Probit - not always
> > the product of the variables includes a variable dummy. For instance,
> > as independent variables some models consider:
> >
> >   y education education(to the square) experience
> > educationXexperience
> >
> >  If I use a command as the mfx - the marginal effect just considers
> > the associated coefficient the education and the correct would be:
> >
> > dy/d.education = 1 + 2.education(mean) + experience (mean) - if I use
> > mfx with medium effects.
> >
> >  Some statistical packages allow logical (as product of terms)
> > operations and later the linearization of the equations. In Stata, I
> > already have to do that creating variables before defining the
> > equation. To estimate the equation in itself - that doesn't cause any
> > problem but to establish marginal effects. I easily can calculate the
> > marginal effects manually starting from the equation but I don't have
> > s.e. or intervals of the estimates.
>
> As I said, I was trying to interpret your question. I assumed you had a
> dummy because you refered to logical operations (AND, OR, NOT, which
> result in either true (1) or false (0)) instead of arithmatic
>
> Notice that the formula you give is the marginal effect of education on
>
> the linear predictor (xb) and not on the probability. Below is an
> example that shows you the marginal effect of education (grade) for
> average values on all explanatory variables. However, I don't think
> that it is a good idea to add all kinds of non-linearities to your
> model and than try to summarize the effect with one number. That
> defeats the very purpose of adding those non-linearities. For instance,
> you add interactions because you think that the effect of education is
> different for people with different amounts of experience. By
> summarizing the effect of eductation with 1 number you throw all that
> information away again.
>
> *------------- begin example ---------------------
> sysuse nlsw88, clear
>
>
> local mgr = r(mean)
> sum ttl_exp if e(sample)
> local mttl = r(mean)
>
>
> #delimit ;
> local xb "_b[_cons] +
>          _b[ttl_exp]*`mttl' +
>
>
> nlcom normalden(`xb') *
> #delimit cr
> *-------------- end example ---------------------
> (For more on how to use examples I sent to the Statalist, see
> http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html )
>
> Hope this helps,
> Maarten
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> Buitenveldertselaan 3 (Metropolitan), room Z434
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
>      __________________________________________________________
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>

--
-------------------------------
Joao Ricardo Lima
Professor
UFPB-CCA-DCFS
+553138923914
-------------------------------

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