Last week, a colleague and I were having a conversation about standard errors. He had a new discovery for me - "Did you know that clustered standard errors and robust standard errors are the same thing with panel data?"
I argued that this couldn't be right - but he said that he'd run -xtreg- in Stata with robust standard errors and with clustered standard errors and gotten the same result - and then sent me the relevant citations in the Stata help documentation. I'm highly skeptical - especially when it comes to standard errors - so I decided to dig into this a little further.
Turns out Andrew was wrong after all - but through very little fault of his own. Stata pulled the wool over his eyes a little bit here. It turns out that in panel data settings, "robust" - AKA heteroskedasticity-robust - standard errors aren't consistent. Oops. This important insight comes from James Stock and Mark Watson's 2008 Econometrica paper. So using -xtreg, fe robust- is bad news. In light of this result, StataCorp made an executive decision: when you specify -xtreg, fe robust-, Stata actually calculates standard errors as though you had written -xtreg, vce(cluster panelvar)- !
On the one hand, it's probably a good idea not to allow users to compute robust standard errors in panel settings anymore. On the other hand, computing something other than what users think is being computed, without an explicit warning that this is happening, is less good.
To be fair, Stata does tell you that "(Std. Err. adjusted for N clusters in panelvar)", but this is easy to miss - there's no "Warning - Clustered standard errors computed in place of robust standard errors" label, or anything like that. The help documentation mentions (on p. 25) that specifying -vce(robust)- is equivalent to specifying -vce(cluster panelvar)-, but what's actually going on is pretty hard to discern, I think. Especially because there's a semantics issue here: a cluster-robust standard error and a heteroskedasticity-robust standard error are two different things. In the econometrics classes I've taken, "robust" is used to refer to heteroskedasticity- (but not cluster-) robust errors. In fact, StataCorp refers to errors this way in a very thorough and useful FAQ answer posted online - and clearly states that the Huber and White papers don't deal with clustering in another.
All that to say: when you use canned routines, it's very important to know what exactly they're doing! I have a growing appreciation for Max's requirement that his econometrics students build their own functions up from first principles. This is obviously impractical in the long run, but helps to instill a healthy mistrust of others' code. So: caveat econometricus - let the econometrician beware!