Skipper Seabold <[email protected]> :
The -margins- manual entry is exhaustive and a good place to start,
but in short:
Don't divide by y but by yhat. Particularly for a zero/one depvar,
where dividing by y just sets the negative outcomes to missing
(dividing by zero) and leaves the positives unchanged (dividing by
one). Also no need to use Mata here... and easier to see a mistake
in Stata probably.
clear all
use http://www.ats.ucla.edu/stat/stata/examples/greene/tbl19-1
qui logit grade gpa tuce psi
set type double
predict xb, xb
g f=invlogit(xb)/(1+exp(xb))
g fy=f/grade
predict p
g fp=f/p
su
mat b=r(mean)*e(b)
margins, eydx(*)
mat li b
On Wed, Dec 30, 2009 at 10:50 PM, Skipper Seabold <[email protected]> wrote:
> If eydx is defined as dy/dx * (1/y), then I don't understand how the
> following values are found by margins (using Stata 11):
>
> use http://www.ats.ucla.edu/stat/stata/examples/greene/tbl19-1, clear
> logit grade gpa tuce psi
>
> <snip>
>
> margins, eydx
>
> Average marginal effects Number of obs = 32
> Model VCE : OIM
>
> Expression : Pr(grade), predict()
> ey/dx w.r.t. : gpa tuce 1.psi
>
> ------------------------------------------------------------------------------
> | Delta-method
> | ey/dx Std. Err. z P>|z| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> gpa | 1.854637 .8479031 2.19 0.029 .1927771 3.516496
> tuce | .0624472 .0930901 0.67 0.502 -.1200061 .2449006
> 1.psi | 1.554903 .7283702 2.13 0.033 .127324 2.982483
> ------------------------------------------------------------------------------
>
>
> Presumably it would give something like the following, if I had
> treated psi as continuous (Please excuse the hackish mata. I'm still
> learning my way around...). Are the missing values (NaNs or Infs,
> depending on your take) handled in some odd way or is something
> entirely different going on? I didn't see anything in the manual on
> this or with a (quick) google search.
>
> mata
> exog = st_data(., ("gpa", "tuce", "psi"))
> stata(". gen cons = 1")
> cons = st_data(., "cons")
> exog = (exog,cons)
> params = st_matrix("e(b)")'
> xb = exog * params
> pdf = exp(-xb):/(1:+exp(-xb)):^2
> dydx = pdf * params'
> endog = st_data(., ("grade"))
> mfx = mean(effects :/ endog)
> end
>
> Any clarifications or further references would be appreciated.
>
> Cheers,
> Skipper
>
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