In case your model includes interaction terms, interpretation of results is not straightforward anymore. Due to (at least) two different standard errors, you should be careful in interpreting results as significant. I found a very nice article explaining this in detail. You can find it here: http://pan.oxfordjournals.org/cgi/content/abstract/14/1/63. The article refers to paper of Golder, who has included his relevant do-files on his personal website: http://homepages.nyu.edu/~mrg217/.
In the following, I present a do-file I used to plot marginal effects with the relevant standard errors:
xi: reg y x1 x2 x3 x4 x5 x6
Imagine, y denotes wage, x1 is a gender dummy, x2 is a variable showing years of schooling (0-10) and x3 is de interaction variable between gender and years of schooling.
* Generation of variables needed to construct the graph:
replace nr=. if nr>10
lab var nr “Education level”
* Create full range of marginal effects
* Create full range of standard errors
* Generate confidence intervals at the 95% level
graph twoway (line conb1 nr if nr>0, clwidth(medium) clcolor(blue) clcolor(black)) /*
*/ (line top1 nr if nr>0, clpattern(dash) clwidth(thin) clcolor(black)) /*
*/ (line bottom1 nr if nr>0, clpattern(dash) clwidth(thin) clcolor(black)), /*
*/ ytitle(“Marginal effect of” “gender”) /*
*/ xtitle(“Education level”) /*
*/ yscale(noline) xscale(noline) legend(off) yline(0, lcolor(black)) /*
*/ graphregion(fcolor(white)lcolor(white)icolor(white)ilcolor(white)ilstyle(none)color(white)) /*
*/ plotregion(fcolor(white)lcolor(white)icolor(white)ilcolor(white)ilstyle(none)color(white) )
Via this graph, you can easily see for which years of schooling the gender effect is significant, and for which years of schooling it isn’t.