I want the same stars for significancies in regression output in stargazer as in the "normal output".
I produce data
library("stargazer"); library("lmtest"); library("sandwich")
set.seed(1234)
df <- data.frame(y=1001:1100)
df$x <- c(1:70,-100:-71) + rnorm(100, 0, 74.8)
model <- lm(log(y) ~ x, data=df)
and get some model estimates where the coefficient on x has a p-value of 0.1023
coeftest(model, vcov = vcovHC(model, type="HC3"))
I want to have these results in LaTeX. Based on the same function I calculate heteroscedasticity consistent standard estimates and let stargazer use them.
stderr_HC3_model <- sqrt(diag(vcovHC(model, type = "HC3")))
stargazer(model, se=list(stderr_HC3_model))
The stargazer output has a star at the coefficient indicating significance when alpha=10%. I want stargazer to give the same as the coeftest. (Because of the comparability with Stata where reg L_y x, vce(hc3) gives exactly the coeftest results.)
I played around with the stargazer options p.auto, t.auto which did not help. When I execute "stargazer" I cannot view the underlying code as it is possible in other cases. What to do?
Richards answer helped me. I indicate the steps I used to give out more than one regression (let's say ols_a and ols_b).
ses <- list(coeftest(ols_a, vcov = vcovHC(ols_a, type="HC3"))[,2],
coeftest(ols_b, vcov = vcovHC(ols_b, type="HC3"))[,2])
pvals <- list(coeftest(ols_a, vcov = vcovHC(ols_a, type="HC3"))[,4],
coeftest(ols_b, vcov = vcovHC(ols_b, type="HC3"))[,4])
stargazer(ols_a, ols_b, type="text", p=pvals, se=ses)
You need to provide the p values associated with your coeftest. From the man page.
p a list of numeric vectors that will replace the default p-values for each model. Matched by element names. These will form the basis of decisions about significance stars
The following should work.
test <- coeftest(model, vcov = vcovHC(model, type="HC3"))
ses <- test[, 2]
pvals <- test[, 4]
stargazer(model, type="text", p=pvals, se=ses)
This provides the following.
===============================================
Dependent variable:
---------------------------
log(y)
-----------------------------------------------
x -0.00005
Constant 6.956***
(0.003)
-----------------------------------------------
Observations 100
R2 0.026
Adjusted R2 0.016
Residual Std. Error 0.027 (df = 98)
F Statistic 2.620 (df = 1; 98)
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01