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# Re: st: Standard errors for partial effects

 From Richard Williams 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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