Big Data for 21st Century Economic Statistics

I earlier posted here when the call for papers was announced for the NBER's CRIW meeting on Big Data for 21st Century Economic Statistics. The wheels have been turning, and the meeting will soon transpire. The program is here, with links to papers. [For general info on the CRIW's impressive contributions over the decades, see here.]

Modified CRLB with Differential Privacy

It turns out that with differential privacy the Cramer-Rao lower bound (CRLB) is not achievable (too bad for MLE), but you can figure out what *is* achievable, and find estimators that do the trick. (See the interesting talk here by Feng Ruan, and the associated papers on his web site.) The key point is that estimation efficiency is degraded by privacy. The new frontier seems to me to be this: Let's go beyond stark "privacy" or "no privacy" situations, because in reality there is a spectrum of "epsilon-strengths" of "epsilon-differential" privacy.  (Right?)  Then there is a tension: I like privacy, but I also like estimation efficiency, and the two trade off against each other. So there is a choice to be made, and the optimum depends on preferences.

Berk-Nash Equilibrium and Pseudo MLE

The Berk-White statistics/econometrics tradition is alive and well, appearing now as Berk-Nash equilibrium in cutting-edge economic theory. See for example Kevin He's Harvard job-market paper here and the references therein, and the slides from yesterday's lunch talk by my Penn colleague Yuichi Yamamoto. But the connection between Berk-Nash equilibrium of economic theory and KLIC-minimizing pseudo-MLE of econometric theory is under-developed. When the Berk-Nash people get better acquainted with Berk-White people, good things may happen. Effectively Yuichi is pushing in that direction, working toward characterizing log-run behavior of likelihood maximizers rather than beliefs.