I estimated a negative binomial regression model in R that counts the number of cyberattacks a country initiates per year. My unit of observation is country-year.
Since cyberattacks are a very rare occurrence in my dataset, about 80% of all observations equal 0.
Now I want to calculate and present the marginal effects for my regression. Unfortunately, the effects-package always returns identical values (2.220446e-16).
This is the code I am using:
ModelA <- glm.nb(attacks ~ cinc + maj.fac + I(log(gdp_pc)) + years_since_dispute, data = data) # Now I estimate the maginal effects for my first exlanatory variable, "cinc": effect(term = "cinc", mod = ModelA, typical = median, given.values = c(maj.fac = 1)) # maj.fac is a dummy, so I hold it constant
The outcome is the same, regardless for which explanatory variable and regardless at which level I hold my dummy variable constant:
cinc effect 0.00016 0.055 0.11 0.16 0.22 2.220446e-16 2.220446e-16 2.220446e-16 2.220446e-16 2.220446e-16
Does anyone have an idea what I may be doing wrong?
I assume that the problem lies with the fact that my dependent variable measures a very rare occurrence.
Is there something I can do to get marginal effects that are not totally identical or does anyone have a better idea how I could estimate the marginal effects?
I would really appreciate your help,