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I am running models with plm with re-calculated standard errors. The challenge? I want to turn the models with the updated standards errors into a stargazer regression table.

Here is some sample data


library(plm)
library(stargazer)

df <- data.frame(year= rep(c(2000:2005), each=6),
                 county= rep(c("c1", "c2", "c3", "c4", "c5", "c6"), times=6),
                 temperature= runif(36, min=40, max=80),
                 trade= runif(36, min=10, max=20),
                 policy.outcome= runif(36, min=0, max=100))

df.p <- pdata.frame(df, index=c("county", "year"))

m1 <- plm(policy.outcome
 ~ temperature + trade, data=df.p, model="within", effect="twoways")

summary(m1)

I would like to load this model into stargazer

summary(m1, vcov = function(x) vcovDC(x, type="HC1"))

stargazer(m1, align= T, type = "text")
Improve this question
3
  • What happens when you run your stargazer(m1, ...) code? Does it not return what you want, throw an error, etc? Commented Dec 17, 2024 at 1:38
  • 1
    help("stargazer") specifies how you can pass modified standard errors ... Commented Dec 17, 2024 at 6:09
  • As a side note modelsummary lets you do this in the same call with modelsummary(m1, vcov = \(x) vcovDC(x, type = 'HC1')) Commented Dec 17, 2024 at 13:59
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