IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2401.15205.html
   My bibliography  Save this paper

csranks: An R Package for Estimation and Inference Involving Ranks

Author

Listed:
  • Denis Chetverikov
  • Magne Mogstad
  • Pawel Morgen
  • Joseph Romano
  • Azeem Shaikh
  • Daniel Wilhelm

Abstract

This article introduces the R package csranks for estimation and inference involving ranks. First, we review methods for the construction of confidence sets for ranks, namely marginal and simultaneous confidence sets as well as confidence sets for the identities of the tau-best. Second, we review methods for estimation and inference in regressions involving ranks. Third, we describe the implementation of these methods in csranks and illustrate their usefulness in two examples: one about the quantification of uncertainty in the PISA ranking of countries and one about the measurement of intergenerational mobility using rank-rank regressions.

Suggested Citation

  • Denis Chetverikov & Magne Mogstad & Pawel Morgen & Joseph Romano & Azeem Shaikh & Daniel Wilhelm, 2024. "csranks: An R Package for Estimation and Inference Involving Ranks," Papers 2401.15205, arXiv.org.
    • Handle: RePEc:arx:papers:2401.15205
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2401.15205
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Wilhelm & Magne Mogstad & Azeem Shaikh, 2021. "Finite- and Large-Sample Inference for Ranks using Multinomial Data with an Application to Ranking Political Parties," RF Berlin - CReAM Discussion Paper Series 2132, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    2. Diaa Al Mohamad & Jelle J. Goeman & Erik W. van Zwet, 2022. "Simultaneous confidence intervals for ranks with application to ranking institutions," Biometrics, The International Biometric Society, vol. 78(1), pages 238-247, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lihua Lei, 2024. "Causal Interpretation of Regressions With Ranks," Papers 2406.05548, arXiv.org.
    2. Denis Chetverikov & Daniel Wilhelm, 2024. "Inference for rank-rank regressions," IFS Working Papers WCWP11/24, Institute for Fiscal Studies.
    3. Federico Crippa & Danil Fedchenko, 2024. "Partially Identified Rankings from Pairwise Interactions," Papers 2410.18272, arXiv.org.
    4. Magne Mogstad & Joseph P. Romano & Azeem M. Shaikh & Daniel Wilhelm, 2023. "A Comment on: “Invidious Comparisons: Ranking and Selection as Compound Decisions” by Jiaying Gu and Roger Koenker," Econometrica, Econometric Society, vol. 91(1), pages 53-60, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2401.15205. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.