Diagnostic methods in generalized estimating equations. An empirical study on Italian football financial performance

Anna Crisci ()
Additional contact information
Anna Crisci: University of Naples Federico II

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 4, No 19, 3429-3440

Abstract: Abstract In this study we describe several diagnostic methods for Generalized Estimating Equations approach. The principal idea behind generalized estimating equations is to generalize and extend the usual likelihood score equation for a generalized linear model by including the covariance matrix of the clustered responses. The advantage of generalized estimating equations is that we do not need to specify the whole response distribution, only the mean structure and, with the aim to increase efficiency, the covariance structure consisting of a working correlation matrix along with the variance function defining the mean–variance relationship. The paper investigates, from a methodological point, to the identification of the best subset of variables, considering the link between the coefficient of determination and Wald Statistics. Some diagnostic measures and a simulated envelope for checking the adequacy of GEE method will be presented. In particular, diagnostic measures are considered and applied to a dataset to assess the impact that some economic-financial variables have on the points made by football teams participating in the Series A League.

Keywords: GEE; Diagnostic measures; Deletion diagnostics; Best model; Half-normal plot; Football performance (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11135-025-02117-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:59:y:2025:i:4:d:10.1007_s11135-025-02117-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-025-02117-7

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().