The DOI matches its Crossref record.
Anatomy of the Selection Problem
Abstract
This article considers anew the problem of estimating a regression E(y|x) when realizations of (y, x) are sampled randomly but y is observed selectively. The central issue is the failure of the sampling process to identify E(y|x). The problem faced by the researcher is to find correct prior restrictions which, when combined with the data, identify the regression. Two kinds of restrictions are examined here. One, which has not been studied before, is a bound on the support of y. Such a bound implies a simple, useful bound on E(y|x). The other, which has received much attention, is a separability restriction derived from a latent variable model.
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Citations
Cited by
Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools
Altonji, J. G., Elder, T. E., & Taber, C. R. (2005) · Journal of Political Economy · doi:10.1086/426036 · Cited by 3,706
Dataset Shift in Machine Learning
(2008) · doi:10.7551/mitpress/9780262170055.001.0001 · Cited by 1,553
A More Credible Approach to Parallel Trends
Rambachan, A., & Roth, J. (2023) · Review of Economic Studies · doi:10.1093/restud/rdad018 · Cited by 1,559
Controlling for endogeneity with instrumental variables in strategic management research
Bascle, G. (2008) · Strategic Organization · doi:10.1177/1476127008094339 · Cited by 1,133
Natural and Quasi-Experiments in Economics
Meyer, B. D. (1995) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1995.10524589 · Cited by 1,100
Metadata
- Authors
- Manski, C. F.
- Venue
- The Journal of Human Resources
- Publisher
- JSTOR
- Published
- 1989
- Volume
- 24
- Issue
- 3
- Pages
- 343
- ISSN
- 0022-166X (ISSN-L)
- OpenAlex
- W1574539612
- Cited by
- 535
- References
- 0
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Manski, C. F. (1989). Anatomy of the Selection Problem. The Journal of Human Resources. https://doi.org/10.2307/145818
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
