IDEAS home Printed from https://ideas.repec.org/a/eee/ecosta/v35y2025icp41-54.html
   My bibliography  Save this article

Joint Hypothesis Testing from Heterogeneous Samples under Cross-dependence

Author

Listed:
  • Hassler, Uwe
  • Hosseinkouchack, Mehdi

Abstract

A testing principle is introduced that allows to combine evidence from N potentially correlated samples. It builds on a (weighted) sum of entities from the individual samples, which is fed into a self-normalizing variance ratio type statistic. Due to self-normalization the (autoco)variances within each sample as well as the cross-covariances between the samples melt into one scaling parameter that cancels from the ratios asymptotically. Tests constructed from this principle are hence robust with respect to cross-dependence without having to estimate any nuisance parameters. The weighting and the entities from the individual samples depend on the testing problem at hand. Two cases are discussed in detail. The first one are tests of restrictions on a parameter vector (e. g. testing restrictions on expected values), while the second one focusses on time series: panel integration tests (unit root as well as stationarity tests). The validity of the asymptotic theory in finite samples is established by means of simulation evidence.

Suggested Citation

  • Hassler, Uwe & Hosseinkouchack, Mehdi, 2025. "Joint Hypothesis Testing from Heterogeneous Samples under Cross-dependence," Econometrics and Statistics, Elsevier, vol. 35(C), pages 41-54.
    • Handle: RePEc:eee:ecosta:v:35:y:2025:i:c:p:41-54
    DOI: 10.1016/j.ecosta.2022.07.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2452306222000818
    Download Restriction: Full text for ScienceDirect subscribers only. Contains open access articles
    File URL: https://libkey.io/10.1016/j.ecosta.2022.07.004?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.

    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:eee:ecosta:v:35:y:2025:i:c:p:41-54. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/econometrics-and-statistics .

    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.