Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model
Name
LECR_A_2604682_O.pdf
Size
1.69 MB
Format
Adobe PDF
Checksum (MD5)
2de52fbab17c0ad2fa8b9692b880e634
Author(s)
Moon, Sarah
Date Issued
December 8, 2025
Journal
Econometric Reviews
Publisher
Taylor & Francis
Citation
Moon, S. (2026). Partial identification of individual-level parameters using aggregate data in a nonparametric model. Econometric Reviews, 1–21.
Version
Final published version
Abstract
I develop a methodology to partially identify linear combinations of conditional mean outcomes when the researcher only has access to aggregate data. Unlike the existing literature, I only allow for marginal, not joint, distributions of covariates in my model of aggregate data. Bounds are obtained by solving an optimization program and can easily accommodate additional polyhedral shape restrictions. I provide a procedure to construct confidence intervals on the identified set and demonstrate the performance of my method in a simulation study. In an empirical illustration of the method using Rhode Island standardized exam data, I find that conditional pass rates vary across student subgroups and across counties.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/07474938.2025.2604682
