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Re: st: Standard errors for partial effects


From   Richard Williams <Richard.A.Williams.5@ND.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Standard errors for partial effects
Date   Fri, 25 May 2007 16:42:36 -0500

At 11:42 AM 5/25/2007, Mentzakis, Emmanouil wrote:
A) On how to compute the std. Errors of the APEs manually.
Can't help you on that one, but you may be able to figure it out by looking at the code for margeff.


B) On why the difference between the standard errors of the APEs and the
marginal effects is so large, giving completely different results
regarding the significance of the estimates.
auto is kind of a wimpy dataset and I've found that it often doesn't work too well with multi-equation models, as the data are being spread too thin. Indeed, if you substitute oprobit for gologit2 in your example, you find that the model doesn't converge. I'd suggest using a larger data set for your testing. In the following example, you'll see that the differences between the APES and the marginal effects are much smaller than what you found. I don't know if that is usually the case so you might try a couple more examples. Note that I use my mfx2 command at the end which gives you all the marginal effects (albeit at a painfully slow pace compared to margeff).

clear
use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";
clonevar x1 = yr89
clonevar x2 = male
gen y = warm
keep if y <=3
gologit2 y x1 x2 , link(probit)
margeff
predictnl ape1 = normprob(- (xb(1) + (_b[1:x1]*(1-x1)))) - normprob(- (xb(1) - (_b[1:x1]*x1))), se(se1)
sum ape1 se1
margeff, at(mean)
mfx2, nolog




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Richard Williams, Notre Dame Dept of Sociology
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