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Partial identification in applied research: benefits and challenges

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  • Kate Ho
  • Adam Rosen

Abstract

Advances in the study of partial identification allow applied researchers to learn about parameters of interest without making assumptions needed to guarantee point identification. We discuss the roles that assumptions and data play in partial identification analysis, with the goal of providing information to applied researchers that can help them employ these methods in practice. To this end, we present a sample of econometric models that have been used in a variety of recent applications where parameters of interest are partially identified, highlighting common features and themes across these papers. In addition, in order to help illustrate the combined roles of data and assumptions, we present numerical illustrations for a particular application, the joint determination of wages and labor supply. Finally we discuss the benefits and challenges of using partially identifying models in empirical work and point to possible avenues of future research.

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  • Kate Ho & Adam Rosen, 2016. "Partial identification in applied research: benefits and challenges," CeMMAP working papers 45/16, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:45/16
    DOI: 10.1920/wp.cem.2016.4516
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    1. 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.
    2. Choudhury, Sanchari, 2019. "WTO membership and corruption," European Journal of Political Economy, Elsevier, vol. 60(C).
    3. Sarah Moon, 2024. "Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model," Papers 2403.07236, arXiv.org, revised May 2024.
    4. Christian Bontemps & Thierry Magnac, 2017. "Set Identification, Moment Restrictions, and Inference," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 103-129, September.
    5. Sam Asher & Paul Novosad & Charlie Rafkin, 2018. "Partial Identification of Expectations with Interval Data," Papers 1802.10490, arXiv.org.
    6. Wang, Zi, 2021. "Headquarters gravity: How multinationals shape international trade," Journal of International Economics, Elsevier, vol. 131(C).
    7. Matthew A. Masten & Alexandre Poirier, 2020. "Inference on breakdown frontiers," Quantitative Economics, Econometric Society, vol. 11(1), pages 41-111, January.
    8. Siwach, Garima, 2017. "Criminal background checks and recidivism: Bounding the causal impact," International Review of Law and Economics, Elsevier, vol. 52(C), pages 74-85.

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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