IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2203.01425.html
   My bibliography  Save this paper

A Modern Gauss-Markov Theorem? Really?

Author

Listed:
  • Benedikt M. Potscher
  • David Preinerstorfer

Abstract

We show that the theorems in Hansen (2021a) (the version accepted by Econometrica), except for one, are not new as they coincide with classical theorems like the good old Gauss-Markov or Aitken Theorem, respectively; the exceptional theorem is incorrect. Hansen (2021b) corrects this theorem. As a result, all theorems in the latter version coincide with the above mentioned classical theorems. Furthermore, we also show that the theorems in Hansen (2022) (the version published in Econometrica) either coincide with the classical theorems just mentioned, or contain extra assumptions that are alien to the Gauss-Markov or Aitken Theorem.

Suggested Citation

  • Benedikt M. Potscher & David Preinerstorfer, 2022. "A Modern Gauss-Markov Theorem? Really?," Papers 2203.01425, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:2203.01425
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2203.01425
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Stephen Portnoy, 2022. "Linearity of Unbiased Linear Model Estimators," The American Statistician, Taylor & Francis Journals, vol. 76(4), pages 372-375, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Benedikt M. Potscher, 2024. "Comments on B. Hansen's Reply to "A Comment on: `A Modern Gauss-Markov Theorem'", and Some Related Discussion," Papers 2406.03971, arXiv.org.
    2. Bollerslev, Tim & Li, Jia & Li, Qiyuan, 2024. "Optimal nonparametric range-based volatility estimation," Journal of Econometrics, Elsevier, vol. 238(1).

    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 & Jeffrey Wooldridge, 2022. "What Estimators Are Unbiased For Linear Models?," Papers 2212.14185, arXiv.org.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

    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:2203.01425. 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.