Welcome
James M. Poterba, Massachusetts Institute of Technology

Empirical Bayes Methods: Motivation and Theory (slides)
Jiaying Gu, University of Toronto
Topics:
• 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

Empirical Bayes Applications (slides)
Christopher R. Walters, University of California, Berkeley and NBER
Topics:
• 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

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.