Thursday, July 22, 2010

Ed Leamer knocks it out of the park (again)

Leamer has a fantastic essay in the Spring 2010 Journal of Economic Perspectives (ungated version here) reacting to Angrist & Pishke's semi-triumphalist piece on the "revolution" in applied econometrics. My favorite part, reproduced below, hits on something I complain about to my students all the time, namely the practice of eschewing any attempt to obtain more accurate point estimates in favor of using confidence intervals that only have an asymptotic justification despite having a decidedly finite sample. Anyway, I'll just shut up and let Ed preach it:

It should not be a surprise at this point in this essay that I part ways with Angrist and Pischke in their apparent endorsement of White’s (1980) paper on how to calculate robust standard errors. Angrist and Pischke write: “Robust standard errors, automated clustering,
and larger samples have also taken the steam out of issues like heteroskedasticity and serial correlation. A legacy of White’s (1980) paper on robust standard errors, one of the most highly cited from the period, is the near-death of generalized least squares in cross-sectional applied work.”

An earlier generation of econometricians corrected the heteroskedasticity problems with weighted least squares using weights suggested by an explicit heteroskedasticity model.
These earlier econometricians understood that reweighting the observations can have dramatic effects on the actual estimates, but they treated the effect on the standard errors as a secondary matter. A “robust standard” error completely turns this around, leaving the estimates the same but changing the size of the confidence interval.

Why should one worry about the length of the confidence interval, but not the location? This mistaken advice relies on asymptotic properties of estimators. I call it “White-washing.” Best to remember that no matter how far we travel, we remain always in the Land of the Finite
Sample, infinitely far from Asymptopia. Rather than mathematical musings about life in Asymptopia, we should be doing the hard work of modeling the heteroskedasticity and the time dependence to determine if sensible reweighting of the observations materially changes the locations of the estimates of interest as well as the widths of the confidence

Yes! Amen!