Video Tutorial on Robust Standard Errors

Update: I have included a modified version of this summaryR() command as part of my package tonymisc, which extends mtable() to report robust standard errors. The tonymisc package is available on CRAN through the install.packages() command.

If you have the right R commands at your disposal, it is simple to correct for heteroskedasticity using the robust correction that is commonly-used among economists. I recorded a video tutorial to describe the simplest (and most flexible) way I know to get R to compute robust standard errors.

The key is to use a "summary-style" command that has an option to correct for heteroskedasticity. The command I like to use is called summaryR(). Here is the script file with the summaryR() command.

	## ---------------------------------------------------------------------------------------- ##
	## Author: John Fox ##
	## Source: http://r.789695.n4.nabble.com/R-extend-summary-lm-for-hccm-td815004.html ##
	## Adapted by Tony Cookson. ##
	## -- Only Change Made: Changed the name of the function (unwisely maybe) ##
	## to summaryR from summaryHCCM.lm. I also changed the spelling of consistent ##
	## ---------------------------------------------------------------------------------------- ##
	summaryR.lm <- function(model, type=c("hc3", "hc0", "hc1", "hc2", "hc4"), ...){
	if (!require(car)) stop("Required car package is missing.")
	type <- match.arg(type)
	V <- hccm(model, type=type)
	sumry <- summary(model)
	table <- coef(sumry)
	table[,2] <- sqrt(diag(V))
	table[,3] <- table[,1]/table[,2]
	table[,4] <- 2*pt(abs(table[,3]), df.residual(model), lower.tail=FALSE)
	sumry$coefficients <- table
	p <- nrow(table)
	hyp <- cbind(0, diag(p - 1))
	sumry$fstatistic[1] <- linearHypothesis(model, hyp,white.adjust=type)[2,"F"]
	print(sumry)
	cat("Note: Heteroscedasticity-consistent standard errors using adjustment", type, "\n")
	}

view raw summaryR.R hosted with ❤ by GitHub

I found this function on an R-help discussion board where several people were answering someone's question about extending the summary.lm() command.

I deserve none of the credit for writing this (credit goes to John Fox), but I consider it my duty to point out how nice this function is. I demonstrate how to use the function in this video



Here are is the script file I used in the video:

	## -------------------------------------- ##
	## R Tutorial on Robust Standard Errors ##
	## Author: Tony Cookson ##
	## -------------------------------------- ##
	## Read Data
	library(foreign)
	ed = read.dta("C://R//edudat2.dta")
	## Obtain regression object
	educ.lm = lm(log(wage)~gender + educ + pareduc, data=ed)
	## Summarize Output Assuming Homoskedasticity
	summary(educ.lm)
	## Need to load the car library
	library(car)
	## If you don't have car installed you'll need to install it
	## Use this command:
	## install.packages("car", dependencies = TRUE)
	## Then, run the library command library(car)
	## Robust Standard errors
	summaryR(educ.lm) ## Special Function written by John Fox
	## Can Specify Type of Heteroskedasticity correction
	summaryR(educ.lm, type="hc0") ## White SE
	summaryR(educ.lm, type="hc1") ## Stata's Default
	summaryR(educ.lm, type="hc2") ## Unbiased under homoskedasticity
	summaryR(educ.lm, type="hc3") ## Default (conservative)
	## Test using linearHypothesis
	linearHypothesis(educ.lm, "educ+pareduc =1", white.adjust="hc3")
	linearHypothesis(educ.lm, "educ+pareduc =1", white.adjust="hc0")

view raw RobustSETutorial.R hosted with ❤ by GitHub

Here's a link to the data set.