Hi, I have fit a xtlogit model with an interaction term between two variables (x1 & x2). x1 is a dummy variable and x2 is continuous taking values of (0,1,2,3). I found a highly significant marginal effect of x2 using "margins, dydx(x2) at (x1=0)." However, when I get linear predictions for x2=0 and x2=3 respectively while holding x1 constant by running

margins, at(x2==0 x1==0)

margins, at(x2==3 x1==0)

the gap between their outputs is exactly 3 times of the marginal effect previously obtained using "dydx" option, but their C.I.s are big so that they overlap, which probably means that there is no difference in their predicted y between x2=0 and x2=3 (?). (The same thing happens with "predict (pu0)" option.) I expected when the marginal effect is statistically significant, their C.I.s will "not" overlap. So, my question is, can I still say that there is a marginal effect of x2 when x1==0?

Also, when I am using "margins, at(varlist)," do I have to specify values for all variables included in my regression? or can I specify only the ones that I want to fix at specific values, then does Stata automatically set other unspecified variables at mean?

I hope to hear your advice asap. Thank you very much in advance!

margins, at(x2==0 x1==0)

margins, at(x2==3 x1==0)

the gap between their outputs is exactly 3 times of the marginal effect previously obtained using "dydx" option, but their C.I.s are big so that they overlap, which probably means that there is no difference in their predicted y between x2=0 and x2=3 (?). (The same thing happens with "predict (pu0)" option.) I expected when the marginal effect is statistically significant, their C.I.s will "not" overlap. So, my question is, can I still say that there is a marginal effect of x2 when x1==0?

Also, when I am using "margins, at(varlist)," do I have to specify values for all variables included in my regression? or can I specify only the ones that I want to fix at specific values, then does Stata automatically set other unspecified variables at mean?

I hope to hear your advice asap. Thank you very much in advance!

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