A Generalized Estimating Equation in Longitudinal Data to Determine an Efficiency Indicator for Football Teams

被引:0
作者
Anna Crisci
Luigi D’Ambra
Vincenzo Esposito
机构
[1] Pegaso Telematic University,Department of Law and Economic Sciences
[2] University of Naples,Department Economics, Management and Institutions
[3] Federico II,undefined
[4] Quadrans S.R.L,undefined
来源
Social Indicators Research | 2019年 / 146卷
关键词
Financial indicator; Generalized estimating equations; Best subset; Mallows;
D O I
暂无
中图分类号
学科分类号
摘要
Over the years football has attracted enormous interest from various fields of study, attracting attention both for its sporting and social aspects. Professional business operators consider football an important industry with enormous potential both in terms of its size and growth, and also because of indirect benefits due to the popularity gained by investors and management of football teams. The focus of the analysis has been on what characterizes most football clubs, and determines their particular economic and financial needs. The aim of this paper is to establish an efficiency measurement for football team financial resource allocation. In particular, we analysed the impact that the income statement, Net equity and Team value variables have on the points achieved by football teams playing in “Serie A” championship (Italian league). The method used in our study is a generalized estimating equation (GEE) for longitudinal count data. In addition we consider a coefficient of determination in the GEE approach based on Wald Statistics, and we propose a modified Mallow’s Cp for choosing the best model. Finally we propose an AFRSport index based on the differences between observed and theoretical points, in order to identify those teams that efficiently employ their financial resources.
引用
收藏
页码:249 / 261
页数:12
相关论文
共 27 条
  • [1] Forrester D(2002)Outcome uncertainty and attendance demand in sport: The Case of English soccer Journal of the Royal Statistical Society: Series D (The Statistician) 51 229-241
  • [2] Simmons R(2011)Criterion for the selection of a working correlation structure in the generalized estimating equation approach for longitudinal balanced data Communications in Statistics Theory and Methods 40 3839-3856
  • [3] Gosho M(1986)A note on the transformation of Chi squared variables to normality The American Statistician 40 296-298
  • [4] Hamada C(2007)Criteria for working-correlation-structure selection in GEE: Assessment via simulation The American Statistician 61 360-364
  • [5] Yoshimura I(2009)Working-correlation-structure identification in generalized estimating equations Statistics in Medicine 28 642-658
  • [6] Hawkins DM(1986)Longitudinal data analysis using generalized linear models Biometrika 73 13-22
  • [7] Wixley RAJ(1973)Some comments on CP Technometrics 7 175-190
  • [8] Hin LY(2007)A measure of partial association for generalize estimatiing equations Statistical Modelling 57 120-125
  • [9] Carey VJ(2001)Akaike’s information criterion in generalized estimating equations Biometrics 46 441-452
  • [10] Wang YG(2004)Model diagnostic plots for repeated measure data Biometrical Journal 77 485-497