IDEAS home Printed from https://ideas.repec.org/a/oup/restud/v92y2025i1p238-267..html
   My bibliography  Save this article

Partially Linear Models under Data Combination

Author

Listed:
  • X D’Haultfœuille
  • C Gaillac
  • A Maurel

Abstract

We study partially linear models when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using recent tools from optimal transport theory, we derive a constructive characterization of the sharp identified set. We then build on this result and develop a novel inference method that exploits the specific geometric properties of the identified set. Our method exhibits good performances in finite samples, while remaining very tractable. We apply our approach to study intergenerational income mobility over the period 1850–1930 in the U.S. Our method allows us to relax the exclusion restrictions used in earlier work, while delivering confidence regions that are informative.

Suggested Citation

  • X D’Haultfœuille & C Gaillac & A Maurel, 2025. "Partially Linear Models under Data Combination," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 92(1), pages 238-267.
    • Handle: RePEc:oup:restud:v:92:y:2025:i:1:p:238-267.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/restud/rdae022
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:oup:restud:v:92:y:2025:i:1:p:238-267.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/restud .

    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.