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Practical and theoretical advances in inference for partially identified models

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  • Ivan A. Canay

    (Institute for Fiscal Studies and Northwestern University)

  • Azeem M. Shaikh

    (Institute for Fiscal Studies and University of Chicago)

Abstract

This paper surveys some of the recent literature on inference in partially identified models. After reviewing some basic concepts, including the definition of a partially identified model and the identified set, we turn our attention to the construction of confidence regions in partially identified settings. In our discussion, we emphasize the importance of requiring confidence regions to be uniformly consistent in level over relevant classes of distributions. Due to space limitations, our survey is mainly limited to the class of partially identified models in which the identified set is characterized by a finite number of moment inequalities or the closely related class of partially identified models in which the identified set is a function of a such a set. The latter class of models most commonly arise when interest focuses on a subvector of a vector valued parameter, whose values are limited by a finite number of moment inequalities. We then rapidly review some important parts of the broader literature on inference in partially identified models and conclude by providing some thoughts on fruitful directions for future research.

Suggested Citation

  • Ivan A. Canay & Azeem M. Shaikh, 2016. "Practical and theoretical advances in inference for partially identified models," CeMMAP working papers CWP05/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:05/16
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    Cited by:

    1. Ho, Kate & Rosen, Adam M., 2015. "Partial Identification in Applied Research: Benefits and Challenges," CEPR Discussion Papers 10883, C.E.P.R. Discussion Papers.
    2. Battey, Heather & Feng, Qiang & Smith, Richard J., 2016. "Improving confidence set estimation when parameters are weakly identified," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 117-123.
    3. Peter Z. Schochet, 2020. "The Complier Average Causal Effect Parameter for Multiarmed RCTs," Evaluation Review, , vol. 44(5-6), pages 410-436, October.
    4. Oliver R. Cutbill & Rami V. Tabri, 2022. "The Impossibility of Testing for Dependence Using Kendall’s Ƭ Under Missing Data of Unknown Form," Working Papers 2022-03, University of Sydney, School of Economics.
    5. Matthew A. Masten & Alexandre Poirier, 2020. "Inference on breakdown frontiers," Quantitative Economics, Econometric Society, vol. 11(1), pages 41-111, January.
    6. Matthew A. Masten & Alexandre Poirier, 2018. "Identification of Treatment Effects Under Conditional Partial Independence," Econometrica, Econometric Society, vol. 86(1), pages 317-351, January.
    7. Vishal Kamat, 2017. "Identifying the Effects of a Program Offer with an Application to Head Start," Papers 1711.02048, arXiv.org, revised Aug 2023.
    8. Machado, Cecilia & Shaikh, Azeem M. & Vytlacil, Edward J., 2019. "Instrumental variables and the sign of the average treatment effect," Journal of Econometrics, Elsevier, vol. 212(2), pages 522-555.
    9. Matthew Masten & Alexandre Poirier, 2016. "Partial independence in nonseparable models," CeMMAP working papers 26/16, Institute for Fiscal Studies.
    10. Dzemski, Andreas & Okui, Ryo, 2018. "Confidence Set for Group Membership," Working Papers in Economics 727, University of Gothenburg, Department of Economics.
    11. Andreas Dzemski & Ryo Okui, 2017. "Confidence set for group membership," Papers 1801.00332, arXiv.org, revised Nov 2023.

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    Keywords

    Partially Identified Model; Confidence Regions; Uniform Asymptotic Validity; Moment Inequalities; Subvector Inference;
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