CONSTRAINT QUALIFICATIONS IN PARTIAL IDENTIFICATION
Open accessAbstract
The literature on stochastic programming typically restricts attention to problems that fulfill constraint qualifications. The literature on estimation and inference under partial identification frequently restricts the geometry of identified sets with diverse high-level assumptions. These superficially appear to be different approaches to closely related problems. We extensively analyze their relation. Among other things, we show that for partial identification through pure moment inequalities, numerous assumptions from the literature essentially coincide with the Mangasarian–Fromowitz constraint qualification. This clarifies the relation between well-known contributions, including within econometrics, and elucidates stringency, as well as ease of verification, of some high-level assumptions in seminal papers.
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- Authors
- Kaido, H., Molinari, F., & Stoye, J.
- Affiliations
- Boston University (US)
- Cornell University (US)
- Corresponding author
- Stoye, Jörg
- Venue
- Econometric Theory
- Publisher
- Cambridge University Press (CUP)
- Published
- 2021-06-04
- Volume
- 38
- Issue
- 3
- Pages
- 596-619
- ISSN
- 0266-4666 (ISSN-L)
- 1469-4360
- OpenAlex
- W2969222406
- Cited by
- 16
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 1 · 2020: 2 · 2019: 1 · 2018: 2
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Econometric Theory (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OpenBU (Boston University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic theories and models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- partial identification, constraint qualification, moment inequalities, identified set, stochastic programming
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