IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v41y2022i10p1265-1286.html
   My bibliography  Save this article

Testing rank similarity in the local average treatment effects model

Author

Listed:
  • Ju Hyun Kim
  • Byoung G. Park

Abstract

This paper develops a test for the rank similarity condition of the nonseparable instrumental variable quantile regression model using the local average treatment effect model. When the instrument takes more than two values or multiple binary instruments are available, there exist multiple complier groups for which the marginal distributions of potential outcomes are identified. A testable implication is obtained by comparing the distributions of ranks across complier groups. We propose a test procedure in a semiparametric quantile regression specification. We establish the weak convergence of the test statistic and the validity of the bootstrap critical value. We illustrate the test with an empirical example of the effects of fertility on women’s labor supply.

Suggested Citation

  • Ju Hyun Kim & Byoung G. Park, 2022. "Testing rank similarity in the local average treatment effects model," Econometric Reviews, Taylor & Francis Journals, vol. 41(10), pages 1265-1286, November.
    • Handle: RePEc:taf:emetrv:v:41:y:2022:i:10:p:1265-1286
    DOI: 10.1080/07474938.2022.2114624
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07474938.2022.2114624
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07474938.2022.2114624?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    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:taf:emetrv:v:41:y:2022:i:10:p:1265-1286. 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.