A re-tweet from a colleague whom I follow on Twitter brought an important paper to my attention. I thought I’d share it more widely.
“Econ friends, @jepusto and I have a new paper out that we would love to share. It’s about clustering your standard errors (more below).
Any suggestions for how to get these methods out to economists given that we aren’t NBER?
Summary: Our paper provides small-sample adjustments to cluster robust variance estimation (CRVE). It can be used with panel data, experimental data, and regression. You can implement the method in a Stata macro called REG_SANDWICH and an R package called clubSandwich.
Why do you need this? Regular CRVE doesn’t do so well, even with as many as 100 clusters (!). In fact, CRVE only gives you appropriate Type I error when your covariates are balanced.
What did we do? We extended the bias-robust linearization method (BRL) by Bell & McCaffrey in three ways: (1) in addition to a t-test, there is now an F-test; (2) We can handle the inclusion of fixed effects; (3) You get the same results whether you use FE or absorption.
How does it work? The adjustment inflates the standard errors a small bit. But more importantly, it provides Satterthwaite-type degrees of freedom that are more appropriate. The result is a test we call the ‘Approximate Hotelling’s T-squared’ (AHT) test.
We’d love to share the work broadly, so if you have ideas, please let us know. Thanks!”
- Cluster-robust variance estimation with clubSandwich
- Meta-analysis with cluster-robust variance estimation
- Cluster-robust standard errors and hypothesis tests in panel data models