--- 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
operations (addition, multiplication, etc).
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
gen grade2 = grade^2
gen ttl_expXgrade = ttl_exp*grade
probit union grade grade2 ttl_exp ttl_expXgrade
sum grade if e(sample)
local mgr = r(mean)
sum ttl_exp if e(sample)
local mttl = r(mean)
#delimit ;
local xb "_b[_cons] +
_b[grade]*`mgr' +
_b[grade2]*`mgr'^2 +
_b[ttl_exp]*`mttl' +
_b[ttl_expXgrade]*`mttl'*`mgr' " ;
nlcom normalden(`xb') *
(_b[grade] + 2*_b[grade2]*`mgr' +
_b[ttl_expXgrade]*`mttl') ;
#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
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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