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Whitewashing your standard errors

Great quote from Gary King warning about the dangers of the all-to-common " , robust " (or I guess it's ( , vce(robust) now) solution for whitewashing the SEs in your model :

"[...] if robust and classical standard errors diverge—which means the author acknowledges that one part of his or her model is wrong—then why should readers believe that all the other parts of the model that have not been examined are correctly specified? We normally prefer theories that come with measures of many validated observable implications; when one is shown to be inconsistent with the evidence, the validity of the whole theory is normally given more scrutiny, if not rejected (King, Keohane, and Verba 1994). Statistical modeling works the same way: each of the standard diagnostic tests evaluates an observable implication of the statistical model. The more these observable implications are evaluated, the better, since each one makes the theory vulnerable to being proven wrong. This is how science progresses. According to the contrary philosophy of science implied by the most common use of robust standard errors, if it looks like a duck and smells like a duck, it is just possible that it could be a beautiful blue-crested falcon." 

Source:  https://gking.harvard.edu/files/gking/files/robust_0.pdf 

It's no panacea, but that's not what I thought based on grad school econometrics courses.
This article makes me hungry for a sandwich....

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