Cluster-Robust Standard Errors in Stargazer

Question

Does anyone know how to get stargazer to display clustered SEs for lm models? (And the corresponding F-test?) If possible, I'd like to follow an approach similar to computing heteroskedasticity-robust SEs with sandwich and popping them into stargazer as in http://jakeruss.com/cheatsheets/stargazer.html#robust-standard-errors-replicating-statas-robust-option.

I'm using lm to get my regression models, and I'm clustering by firm (a factor variable that I'm not including in the regression models). I also have a bunch of NA values, which makes me think multiwayvcov is going to be the best package (see the bottom of landroni's answer here - Double clustered standard errors for panel data - and also https://sites.google.com/site/npgraham1/research/code)? Note that I do not want to use plm.

Edit: I think I found a solution using the multiwayvcov package...

library(lmtest) # load packages
library(multiwayvcov)

data(petersen) # load data
petersen$z <- petersen$y + 0.35  # create new variable

ols1 <- lm(y ~ x, data = petersen) # create models
ols2 <- lm(y ~ x + z, data = petersen)

cl.cov1 <- cluster.vcov(ols1, data$firmid) # cluster-robust SEs for ols1
cl.robust.se.1 <- sqrt(diag(cl.cov1))
cl.wald1 <- waldtest(ols1, vcov = cl.cov1)

cl.cov2 <- cluster.vcov(ols2, data$ticker) # cluster-robust SEs for ols2
cl.robust.se.2 <- sqrt(diag(cl.cov2))
cl.wald2 <- waldtest(ols2, vcov = cl.cov2)

stargazer(ols1, ols2, se=list(cl.robust.se.1, cl.robust.se.2), type = "text") # create table in stargazer

Only downside of this approach is you have to manually re-enter the F-stats from the waldtest() output for each model.

Answer 1

Using the packages lmtest and multiwayvcov causes a lot of unnecessary overhead. The easiest way to compute clustered standard errors in R is the modified summary() function. This function allows you to add an additional parameter, called cluster, to the conventional summary() function. The following post describes how to use this function to compute clustered standard errors in R:

https://economictheoryblog.com/2016/12/13/clustered-standard-errors-in-r/

You can easily the summary function to obtain clustered standard errors and add them to the stargazer output. Based on your example you could simply use the following code:

# estimate models
ols1 <- lm(y ~ x) 

# summary with cluster-robust SEs
summary(ols1, cluster="cluster_id") 

# create table in stargazer
stargazer(ols1, se=list(coef(summary(ols1,cluster = c("cluster_id")))[, 2]), type = "text") 

Answer 2

I would recommend lfe package, which is much more powerful package than lm package. You can easily specify the cluster in the regression model:

ols1 <- felm(y ~ x + z|0|0|firmid, data = petersen)
summary(ols1)

stargazer(OLS1, type="html")

The clustered standard errors will be automatically produced. And stargazer will report the clustered-standard error accordingly.

By the way (allow me to do more marketing), for micro-econometric analysis, felm is highly recommended. You can specify fixed effects and IV easily using felm. The grammar is like:

ols1 <- felm(y ~ x + z|FixedEffect1 + FixedEffect2 | IV | Cluster, data = Data)