Interpretation of Interaction Term in Log-Linear Model

#1
Hello All! I am currently writing my Bachelor’s Thesis and have a question about the interpretation of an interaction term in a log-linear model.
Without an interaction I’d take the coefficient of a x variable and transform it via exp(coefficient)-1 * 100. Then I am able to argue: A 1 unit increase in X is associated with a increase of … percent in Y. The coefficients are just very high (<0.3) That's why I applied the transformation.
Now I want to do that with the coefficient of an interaction term with a continuous and a binary variable. Binary variable is male/female and I want to be able to argue:
A 1 unit increase in the male sample is associated with an increase in variable Y by …percent.
A 1 unit increase in the female sample is associated with an increase in variable Y by …percent.
Is it correct to use the Stata command margins, dydx(continuous variable) at (dummy_variable=(0 1)) and then transform the output for each value of the dummy (0 1) via exp(coefficient)-1 * 100 ?
I would appreciate any help!
 
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hlsmith

Less is more. Stay pure. Stay poor.
#2
This is a cool questions, I plan to look at it tomorrow. Have you found a solutions yet. I feel like I have seen a comparable example in Andrew Gelman's and Jen Hill's Regression Stories book.