IDEAS home Printed from https://ideas.repec.org/h/spr/adschp/978-3-031-97942-2_5.html

Teaching Panel Data Econometrics

In: Teaching Econometrics

Author

Listed:
  • Badi H. Baltagi

    (426 Eggers Hall, Syracuse University, Department of Economics and Center for Policy Research)

Abstract

The theme of this volume, dedicated to a great econometrics teacher, is the teaching of econometrics and its pedagogics. In this spirit, this paper revisits some of the basics in the econometrics of panel data and gives my style of teaching this material using tricks from matrix algebra that I found useful to illustrate their value especially for applied researchers who normally want to skip the derivations and focus on applications. I use the Frisch-Waugh-Lovell theorem discussed in Davidson and MacKinnon (1993, p. 19) to explain why the fixed effects estimator can be obtained from the within regression, which is easier to compute and is the standard practice in panel data software. Next, I use a beautiful trick from Wansbeek and Kapteyn (1982) to derive the random effects estimator as a weighted least squares as suggested by Fuller and Battese (1974). The paper then emphasizes that rejecting the Hausman (1978) specification test based on fixed versus random effects is not necessarily an endorsement of the fixed effects estimator as is done regularly in practice. Rejection of the null means that there is misspecification, and Baltagi (2024) provides alternatives to selecting the fixed effects estimator when the Hausman test rejects. Next, the paper demonstrates that for unbalanced panel data with missing observations, things get messy, and the tricks get fancier, and the students complain.

Suggested Citation

  • Badi H. Baltagi, 2026. "Teaching Panel Data Econometrics," Advanced Studies in Theoretical and Applied Econometrics, in: Eric Hillebrand & William Griffiths (ed.), Teaching Econometrics, pages 91-102, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-97942-2_5
    DOI: 10.1007/978-3-031-97942-2_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:adschp:978-3-031-97942-2_5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.