SI 2022 Methods Lectures - Empirical Bayes Methods, Theory and Application
James M. Poterba, Organizer
July 28, 2022
Skyline CDE
Royal Sonesta Hotel, 40 Edwin H. Land Blvd., Cambridge, MA and on Zoom.us
| Thursday, July 28 | ||
| 3:00 pm |
Welcome James M. Poterba, Massachusetts Institute of Technology |
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| 3:05 pm |
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 |
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| 4:30 pm | Break | |
| 4:40 pm |
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 |
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| 6:00 pm | Adjourn | |
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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. |
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