I figured out that
margins (from R, mimics Stata) is a good way to get
marginal effects (and their significance, CI, ...) for linear models in R.
library("car") library("plm") library("margins") data("LaborSupply", package = "plm") # Linear Regression linear_model <- lm(lnwg ~ kids + age + I(age^2), data = LaborSupply) summary(linear_model ) summary(margins(linear_model))
But it does not support any kind of panel model (found several open/unanswered questions on this).
data("Produc", package = "plm") # Fixed Effects Regression FE_model <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state","year"), method="within") summary(margins(FE_model)) Error in crossprod(beta, t(X)) : non-conformable arguments
How can I calculate marginal effects for a fixed effects model and test their statistical significance manually? What is the best practice workaround? What is the math behind those calculations? Perhaps I can implement a formula in my FE-case. Worst case would be to transform my data and re-do the analysis in