--- On Tue, 16/2/10, Erasmo Giambona wrote:
> Thanks Maarten. In this example, OLS and GLM give very similar
> econimic effects. In fact, 74 cents for the OLS is really
> 9.52% relative to the mean wage of 7.77. This 9.52% is very much
> in line with the 9.7% found with GLM. In my case, the coeff. on X
> for the OLS is 0.0064. Relative to the mean for the LHS variable of
> 0.02. This is an economic effect of about 28%. With the GLS, using
> exactly your code, X gets a coefficient of 2.025 or a 102.5% increase in
> Y. Or perhaps, I am misinterpreting this coefficient.
I have two thoughts about that:
1) If one of your models is badly specified then such differences can
happen. Just graph your data and model and see what is happening.
2) You are probably better of not making such computations by hand, it is
just too easy to get those wrong. There are commands that do it for you,
in Stata 11 you can use the -margins- commands which among others will
compute the average marginal effects, which seems to be what you are
after
*--------------- begin example -----------------
sysuse nlsw88, clear
gen byte baseline =1
reg wage grade
predict yhat_ols
glm wage grade baseline, ///
link(log) eform nocons
margins, dydx(grade)
predict yhat_glm
twoway scatter wage grade, ///
mcolor(gs8) || ///
line yhat* grade, ///
sort ///
legend(order( 1 "raw data" ///
2 "OLS" ///
3 "GLM"))
*--------------- end example --------------------
( For more on how to use examples I sent to statalist see:
http://www.maartenbuis.nl/stata/exampleFAQ.html )
Hope this helps,
Maarten
--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://www.maartenbuis.nl
--------------------------
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