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Simple Inference on Functionals of Set-Identified Parameters Defined by Linear Moments

JoonHwan Cho, Thomas M. Russell

arXiv 7 Oct 2018 · Econometrics · publishedJournal of Business and Economic Statistics (2023) · 9 citations (OpenAlex)

arXiv:1810.03180 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes a new approach to obtain uniformly valid inference for linear functionals or scalar subvectors of a partially identified parameter defined by linear moment inequalities. The procedure amounts to bootstrapping the value functions of randomly perturbed linear programming problems, and does not require the researcher to grid over the parameter space. The low-level conditions for uniform validity rely on genericity results for linear programs. The unconventional perturbation approach produces a confidence set with a coverage probability of 1 over the identified set, but obtains exact coverage on an outer set, is valid under weak assumptions, and is computationally simple to implement.

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43
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Gafarov, B (2021) Inference in high-dimensional set-identified affine models1.000333100%
2Kaido, H. and Santos, A (2014) Asymptotically efficient estimation of models defined by convex moment inequalities0.92843100%
3Fang, Z., Santos, A., Shaikh, A. M., and Torgovitsky, A (2021) Inference for large-scale linear systems with known coefficients0.874202100%
4Kaido, H., Molinari, F., and Stoye, J (2019) Confidence intervals for projections of partially identified parameters0.84333100%
5Kasy, M (2016) Partial identification, distributional preferences, and the welfare ranking of policies0.84333100%
6Spingarn, J. E. and Rockafellar, R. T (1979) The generic nature of optimality conditions in nonlinear programming0.84333100%
7Beresteanu, A. and Molinari, F (2008) Asymptotic properties for a class of partially identified models0.64422100%
8Bontemps, C., Magnac, T., and Maurin, E (2012) Set identified linear models0.64422100%
9Bugni, F. A., Canay, I. A., and Shi, X (2017) Inference for subvectors and other functions of partially identified parameters in moment inequality models0.64422100%
10Chandrasekhar, A. G., Chernozhukov, V., Molinari, F., and Schrimpf, P (2019) Best linear approximations to set identified functions: with an application to the gender wage gap0.64422100%

Showing the top 10 of 43 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Partial Identification in Nonseparable Binary Response Models with Endogenous Regressors We are grateful to James Heckman, Marc Henry, Roger Koenker, and to seminar audiences at Columbia University and Michigan State University for helpful feedback. We also thank Martin Weidner and the organizers of the Chamberlain Seminar, and are grateful to Florian Gunsilius, Sukjin Han, Wayne Gao, and Takuya Ura for their questions and feedback, and to Adam Rosen for his thoughtful discussion. Jiaying Gu acknowledges financial support from the Social Sciences and Humanities Research Council of Canada. All errors are our own1.000214
2The Identification Power of Combining Experimental and Observational Data for Distributional Treatment Effect Parameters1.00053
3Linear programming approach to partially identified econometric models0.87472
4Inference for Linear Conditional Moment Inequalities0.84343
5Simple subvector inference on sharp identified set in affine models0.73732
6Testing Inequalities Linear in Nuisance Parameters0.648114
7When does IV identification not restrict outcomes?0.51132
8Salvaging Falsified Instrumental Variable Models0.40511
9Estimating Discrete Games of Complete Information: Bringing Logit Back in the Game0.40511
10Testing Mechanisms0.40511