View source: R/felm_broom_tidy.R
felm_broom_tidy | R Documentation |
Fast tidying of broom felm output. Note that the function will use clustered standard errors if they are available. Otherwise, the function will use robust standard errors
felm_broom_tidy(felm.mod)
felm.mod |
A model of type |
a data.table with the tidy estimation output
##From felm
library(lfe)
## create covariates
x <- rnorm(1000)
x2 <- rnorm(length(x))
## individual and firm
id <- factor(sample(20,length(x),replace=TRUE))
firm <- factor(sample(13,length(x),replace=TRUE))
## effects for them
id.eff <- rnorm(nlevels(id))
firm.eff <- rnorm(nlevels(firm))
## left hand side
u <- rnorm(length(x))
y <- x + 0.5*x2 + id.eff[id] + firm.eff[firm] + u
## estimate and print result
est <- felm(y ~ x+x2| id + firm)
summary(est, robust = TRUE)
felm_broom_tidy(est)
## With clustered standard errors
est2 <- felm(y ~ x+x2| id + firm | 0 | firm)
summary(est2)
felm_broom_tidy(est2)
# make an example with 'reverse causation'
# Q and W are instrumented by x3 and the factor x4. Report robust s.e.
x3 <- rnorm(length(x))
x4 <- sample(12,length(x),replace=TRUE)
Q <- 0.3*x3 + x + 0.2*x2 + id.eff[id] + 0.3*log(x4) - 0.3*y + rnorm(length(x),sd=0.3)
W <- 0.7*x3 - 2*x + 0.1*x2 - 0.7*id.eff[id] + 0.8*cos(x4) - 0.2*y+ rnorm(length(x),sd=0.6)
# add them to the outcome
y <- y + Q + W
ivest <- felm(y ~ x + x2 | id+firm | (Q|W ~x3+factor(x4)))
summary(ivest,robust=TRUE)
felm_broom_tidy(ivest)
##With clustered standard errors
ivest2 <- felm(y ~ x + x2 | id+firm | (Q|W ~x3+factor(x4)) | firm)
summary(ivest2)
felm_broom_tidy(ivest2)
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