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# Re: st: margins option in Stata10?

 From Richard Williams <[email protected]> To [email protected], [email protected] Subject Re: st: margins option in Stata10? Date Fri, 12 Aug 2011 09:32:45 -0500

```At 06:01 AM 8/12/2011, Andreas Fagereng wrote:
```
```Thanks for the help!

Hopefully I will be able to run it all on Stata11/12 soon.

Andreas
```
```
```
There is also a do it yourself approach for AMEs. In the case of a discrete variable, do something like
```
webuse nhanes2f, clear
* Replicate AME for black without using margins
clonevar xblack = black
logit diabetes i.xblack i.female age, nolog
margins, dydx(xblack)
replace xblack = 0
replace xblack = 1

After the margins and sum commands you get

. margins, dydx(xblack)

Average marginal effects                          Number of obs   =      10335
Model VCE    : OIM

Expression   : Pr(diabetes), predict()
dy/dx w.r.t. : 1.xblack

------------------------------------------------------------------------------
|            Delta-method
|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.xblack |   .0400922   .0087055     4.61   0.000     .0230297    .0571547
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
adjpredwhite |     10335    .0443248    .0362422    .005399   .1358214
adjpredblack |     10335     .084417    .0663927   .0110063   .2436938
meblack |     10335    .0400922    .0301892   .0056073   .1078724

```
I wish the margins command provided an easy way to compute those values for each case. In this instance, I like it because it shows that, while the average marginal effect for black may be .04, across individuals the ME varies from almost 0 to almost .11, i.e., it isn't like every black is 4% more likely to get diabetes than a comparable white. As I show in the presentation I cited earlier, it can be very useful to compute MERs (marginal effects at representative values), e.g. in this case it is useful to show how the marginal effects differ across age levels.
```
```
It is also possible to compute AMEs for continuous variables. Cameron & Trivedi show how in
```
http://www.stata-press.com/books/musr.html

(or at least they did show how in the original version of the book).

-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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