SI 2022 Methods Lectures - Empirical Bayes Methods, Theory and Application

James Poterba, Organizer

July 28, 2022

Skyline CDE

Royal Sonesta Hotel, 40 Edwin H. Land Blvd., Cambridge, MA and on

Conference Code of Conduct

Thursday, July 28
3:00 pm
James M. Poterba, Massachusetts Institute of Technology
3:05 pm
Empirical Bayes Methods: Motivation and Theory (slides)
Jiaying Gu, University of Toronto
• Introduction to EB methods
• Gaussian, Poisson, and duration mixture models
• EB shrinkage and posterior distributions
• Nonparametric EB
• Connections to decision theory
• Frontiers: computation, inference, and prediction
4:30 pm
4:40 pm
Empirical Bayes Applications (slides)
Christopher R. Walters, University of California, Berkeley and NBER
• Teacher and school value-added
• Employer-level labor market discrimination
• Connections to other methods: multi-level/hierarchical models, machine learning, multiple testing, ranking and classification
6:00 pm
Reading list

Angrist, Hull, Pathak, and Walters (2017), “Leveraging lotteries for school value-added: testing and estimation," Quarterly Journal of Economics, 132 (2), 871 – 919.

Gilraine, Gu and McMillan (2022), “A new method for estimating teacher value-added,” NBER working paper 27094.

Gu and Koenker (2017), “Empirical Bayesball remixed: empirical Bayes methods for longitudinal data,” Journal of Applied Econometrics, 32 (3), 575 – 599.

Gu and Koenker (2022), “Invidious comparisons: ranking and selection as compound decisions,” forthcoming Econometrica.

Kline, Rose, and Walters (2022), “Systemic discrimination among large US employers,” forthcoming Quarterly Journal of Economics.

Koenker and Gu (2017), “REBayes: an R package for empirical Bayes mixture methods,” Journal of Statistical Software, 82(1), 1 – 26.