arXiv 10 Jun 2026 · Econometrics
arXiv:2606.12324 · PDF · DOI · OpenAlex · Extracted main text
Economic decisions often depend on many noisy estimates of quantities such as neighborhood effects, school quality, and hospital performance. Shrinkage estimation can improve decisions by pooling information across related units, but geography, adjacency, and shared characteristics each define a different notion of relatedness, and each implies a different way of pooling. We treat the choice of relatedness as part of the estimation problem, using Stein's Unbiased Risk Estimate (SURE) to form a weighted average over a library of flexible shrinkage estimators. This comparison among the candidate estimators treats no prior or latent covariance structure as a correctly specified model for the parameters being estimated. Each candidate is judged by its SURE value. Under smoothness conditions on the estimators, the SURE-weighted average performs nearly as well as the best fixed weighted average of trained candidates, including nonlinear rules whose reported values use the full vector of noisy estimates. In an application to Opportunity Atlas economic mobility data from 20 commuting zones, the best individual spatial specification varies across zones, yet the SURE-weighted average tracks the best in each zone and reduces estimated mean squared error by about 27% relative to the best-performing non-spatial empirical Bayes baseline in our library of estimators.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chetty, R., J. N. Friedman, N. Hendren, M. R. Jones, and S. R. Porter (2026) The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility | 0.843 | 3 | 3 | 100% |
| 2 | Chen, J (2026) Empirical Bayes When Estimation Precision Predicts Parameters | 0.822 | 9 | 6 | 56% |
| 3 | Bellec, P. C. and C.-H. Zhang (2021) Second Order Stein: SURE for SURE and Other Applications in High-Dimensional Inference | 0.737 | 4 | 3 | 50% |
| 4 | Ignatiadis, N. and S. Wager (2019) Covariate-Powered Empirical Bayes Estimation | 0.737 | 3 | 2 | 100% |
| 5 | Chen, J., L. Lei, T. Sudijono, L. Sun, and T. Xie (2025) Compound Selection Decisions: An Almost SURE Approach | 0.693 | 6 | 4 | 33% |
| Chetty | unmatched citation key Chetty | 0.644 | 4 | 1 | 100% |
| 7 | Oliveira, N. L., J. Lei, and R. J. Tibshirani (2024) Unbiased Risk Estimation in the Normal Means Problem via Coupled Bootstrap Techniques | 0.644 | 3 | 2 | 67% |
| 8 | Bergman, P., R. Chetty, S. DeLuca, N. Hendren, L. F. Katz, and C. Pa… (2024) Creating Moves to Opportunity: Experimental Evidence on Barriers to Neighborhood Choice | 0.644 | 2 | 2 | 100% |
| 9 | Fay, R. E. and R. A. Herriot (1979) Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data | 0.644 | 2 | 2 | 100% |
| 10 | Kwon, S (2026) Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 148 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.