IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2610.27224.html

Quantile-Only Regression: Inference and Partial Identification under Quantile Disclosure

Author

Listed:
  • Masamune Iwasawa
  • Hayato Nakanishi

Abstract

Researchers often observe group quantiles but not group means, raising the question of whether these summaries alone support conventional regression analysis. We develop estimation and inference methods for linear models of group means under quantile-only and mixed disclosure. We establish conditions on disclosure design for consistent estimation and asymptotic normality at the parametric rate. Under fixed disclosure, we characterize sharp identified sets and construct simultaneous confidence regions, clarifying how informativeness depends on the number and placement of disclosed quantiles. Applications to education, social care, and medical expenditure illustrate the methods. The R package qonlyreg implements the proposed procedures.

Suggested Citation

  • Masamune Iwasawa & Hayato Nakanishi, 2026. "Quantile-Only Regression: Inference and Partial Identification under Quantile Disclosure," Papers 2610.27224, arXiv.org.
    • Handle: RePEc:arx:papers:2610.27224
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2610.27224
    File Function: Latest version
    Download Restriction: no
    ---><---

    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:2610.27224. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: https://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.