arXiv 2 Oct 2026 · Econometrics
arXiv:2610.04146 · PDF · Extracted main text
Imperfect measurements allow different underlying distributions to generate the same observations. Restrictions linking unobserved components can make it difficult to characterize all compatible distributions and compute the resulting range of parameter values. We ask how to construct computable parameter bounds and tighten them without additional data or stronger assumptions. We develop a general framework for partial identification under imperfect measurement based on distributionally robust optimization. We use measurement restrictions to construct a neighborhood around a benchmark distribution, ensuring that every compatible distribution lies within it. Optimizing over this neighborhood gives parameter bounds through a common procedure that can be adapted to different measurement problems. The choice of benchmark affects how informative these bounds are. We establish conditions under which changing it can tighten the bounds while preserving every parameter value consistent with the same data and assumptions. Applications to networks, missing outcomes, and regression illustrate how the framework tightens bounds and can recover the full range of compatible values.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Blanchet, J. and Murthy, K (2019) Quantifying distributional model risk via optimal transport | 0.585 | 3 | 3 | 33% |
| 2 | Gao, R. and Kleywegt, A (2023) Distributionally robust stochastic optimization with Wasserstein distance | 0.585 | 3 | 3 | 33% |
| 3 | Manski, C. F (1989) Anatomy of the selection problem | 0.511 | 2 | 2 | 50% |
| 4 | Beresteanu, A., Molchanov, I., and Molinari, F (2011) Sharp identification regions in models with convex moment predictions | 0.405 | 1 | 1 | 100% |
| 5 | Beresteanu, A., Molchanov, I., and Molinari, F (2012) Partial identification using random set theory | 0.405 | 1 | 1 | 100% |
| 6 | Chandrasekhar, A. G. and Lewis, R (2016) Econometrics of sampled networks | 0.405 | 1 | 1 | 100% |
| 7 | Christensen, T. and Connault, B (2023) Counterfactual sensitivity and robustness | 0.405 | 1 | 1 | 100% |
| 8 | Duarte, G., Finkelstein, N., Knox, D., Mummolo, J., and Shpitser, I (2024) An automated approach to causal inference in discrete settings | 0.405 | 1 | 1 | 100% |
| 9 | Ekeland, I., Galichon, A., and Henry, M (2010) Optimal transportation and the falsifiability of incompletely specified economic models | 0.405 | 1 | 1 | 100% |
| 10 | Fan, Y., Park, H., Pass, B., and Shi, X (2025) Partial identification in moment models with incomplete data–-a conditional optimal transport approach | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 29 scored citations.