Count Data Regression: Modeling Diversification in Sports Participation in Spain

  1. García, Jaume
  2. Muñiz, Cristina 1
  3. Suárez, María José 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Libro:
Contributions to Economics

ISSN: 1431-1933 2197-7178

ISBN: 9789819949014 9789819949021

Año de publicación: 2024

Páginas: 3-32

Tipo: Capítulo de Libro

DOI: 10.1007/978-981-99-4902-1_1 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Count data models are specifically designed to deal with those cases where the dependent variable is an integer non-negative variable, taking a small number of (low) values, which is the usual situation when the variable to be explained represents the number of times a particular event occurs. This chapter presents an overview of the specific features of the count data models most commonly used in the economic literature, paying special attention to how the zeros are generated. An empirical illustration from the sports economics literature is also provided. Using data from the Spanish Survey of Sporting Habits (2020), individual diversification of sports activity, measured by the number of sports practiced during a year, is studied. The empirical analysis has been implemented using Stata software. It takes into account the specific features of the dependent variable by estimating different count data models, starting with the standard versions used in the microeconometric literature (Poisson and Negative Binomial models) and extending these basic models by considering different specifications in terms of how the zeros are generated. Finally, specific attention is devoted to the interpretation of the estimated coefficients and the calculation of the marginal effects.

Referencias bibliográficas

  • Agrawal M, Sensarma R (2007) Determinants of merger activity: evidence from India. Int J Financ Serv Manag 2(4):277–288. https://doi.org/10.1504/IJFSM.2007.016285
  • Altinisik Y (2022) Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogeneous hurdle model. J App Stat 50(2):408–433. https://doi.org/10.1080/02664763.2022.2096875
  • Altun E (2019) A new model for over-dispersed count data: Poisson quasi-Lindley regression model. Math Sci 13(3):241–247. https://doi.org/10.1007/s40096-019-0293-5
  • Anokye NK, Pokhrel S, Buxton M, Fox-Rushby J (2012) The demand for sports and exercise: results from an illustrative survey. Eur J Health Econ 13(3):277–287. https://doi.org/10.1007/s10198-011-0304-4
  • Bridge MW, Toms MR (2013) The specialising or sampling debate: a retrospective analysis of adolescent sports participation in the UK. J Sport Sci 31(1):87–96. https://doi.org/10.1080/02640414.2012.721560
  • Cabane C, Lechner M (2015) Physical activity of adults: A survey of correlates, determinants and effects. J Econ Stat 235(4–5):376–402. https://doi.org/10.1515/jbnst-2015-4-504
  • Cahoy D, Di Nardo E, Polito F (2021) Flexible models for overdispersed and underdispersed count data. Stat Pap 62(6):2969–2990. https://doi.org/10.1007/s00362-021-01222-7
  • Cameron AC, Trivedi PK (1986) Econometric models based on count data: comparisons and applications of some estimators and tests. J Appl Econom 1(1):29–53. https://doi.org/10.1002/jae.3950010104
  • Cameron AC, Trivedi PK (2005) Microeconometrics: Methods and Applications. Cambridge University Press, New York. https://doi.org/10.1017/CBO9780511811241
  • Cameron AC, Trivedi PK (2014) Regression analysis of count data. Cambridge University Press, New York. https://doi.org/10.1017/CBO9781139013567
  • Cameron AC, Trivedi PK (2022) Microeconometrics using Stata. Volume II: Nonlinear models and causal inference methods, 2nd edn. Stata Press, Texas.
  • Dawson P, Downward P (2011) Participation, spectatorship and media coverage in sport: some initial insights. In: Andreff W (ed) Contemporary issues in sports economics: participation and professional team sports. Edward Elgar, Cheltenham, pp 15–42. https://doi.org/10.4337/9780857930385
  • Downward P, Muñiz C (2019) Sports participation. In: Downward P, Frick B, Humphreys BR, Pawlowski T, Ruseski JE, Soebbing BP (eds) The SAGE handbook of sports economics. SAGE Publications, London, pp 33–44
  • Dupuy JF (2018) Statistical methods for overdispersed count data. Elsevier, Oxford. https://doi.org/10.1016/C2017-0-00831-5
  • Feng CX (2021) A comparison of zero-inflated and hurdle models for modelling zero-inflated count data. J Stat Distrib Appl 8(1):1–19. https://doi.org/10.1186%2Fs40488-021-00121-4
  • Friendly M, Meyer D (2015) Discrete data analysis with R: visualization and modeling techniques for categorical and count data. CRC Press, Boca Raton
  • García J, Muñiz C, Rodríguez P, Suárez MJ (2016) Comparative analysis of sports practice by types of activities. Int J Sport Financ 11(4):327–348
  • García J, Suárez MJ (2020) Organised and non-organised after-school physical activity among children in Spain: the role of school-related variables. Eur Sport Manag Q 20(2):171–188. https://doi.org/10.1080/16184742.2019.1594329
  • García J, Suárez MJ (2021) Dimensions of sports participation: evidence from Mexico. In: Koning RH, Késenne S (eds) A modern guide to sports economics. Edward Elgar, Cheltenham, pp 226–239. https://doi.org/10.4337/9781789906530
  • García J, Suárez MJ (2023) The relevance of specification assumptions when analyzing the drivers of physical activity practice. Econ Model 119:106127. https://doi.org/10.1016/j.econmod.2022.106127
  • Geil P, Million A, Rotte R, Zimmermann KF (1997) Economic Incentives and Hospitalization in Germany. J Appl Econom 12:295–312. https://doi.org/10.1002/(SICI)1099-1255(199705)12:3%3C295::AID-JAE443%3E3.0.CO;2-X
  • Green JA (2021) Too many zeros and/or highly skewed? A tutorial on modelling health behaviour as count data with Poisson and Negative Binomial regression. Health Psyc Behav Med 9(1):436–455. https://doi.org/10.1080/21642850.2021.1920416
  • Harris MN, Zhao X (2007) A zero-inflated ordered probit model, with an application to modelling tobacco consumption. J Econom 141:1073–1099. https://doi.org/10.1016/j.jeconom.2007.01.002
  • Hausman J, Hall BH, Griliches Z (1984) Econometric models for count data with an application to the Patents-R&D relationship. Econometrica 52(4):909–938. https://doi.org/10.2307/1911191
  • Hilbe JM (2014) Modeling count data. Cambridge University Press, Cambridge. https://doi.org/10.1007/978-3-642-04898-2_369
  • Inan T (2021) Using poisson model for goal prediction in European football. J Hum Sport Exerc 16(4):942–955. https://doi.org/10.14198/jhse.2021.164.16
  • Jang TY (2005) Count data models for trip generation. J Transp Eng 131(6):444–450. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:6(444)
  • Lee J, Park CG, Choi M (2016) Regular exercise and related factors in patients with Parkinson’s disease: applying zero-inflated negative binomial modelling of exercise count data. Appl Nurs Res 30:164–169. https://doi.org/10.1016/j.apnr.2015.08.002
  • Lefèvre B, Nohara H, Nier O (2021) Sports practice in Japan and France: a comparative analysis. PLoS ONE 16(6):e0253435. https://doi.org/10.1371%2Fjournal.pone.0253435
  • Lefèvre B, Ohl F (2012) Consuming sports: distinction, univorism and omnivorism. Sport Soc 15(1):44–63. https://doi.org/10.1080/03031853.2011.625276
  • Lefèvre B, Routier G, Llopis-Goig R (2020) Sports participation in France and Spain: an international comparison of voraciousness for sport. Poetics 81:101429. https://doi.org/10.1016/j.poetic.2019.101429
  • Lord D, Geedipally SR, Guilkema SD (2010) Extension of the application of the Conway-Maxwell-Poisson models: analyzing traffic crash data exhibiting underdispersion. Risk Anal 30(8):1268–1276. https://doi.org/10.1111/j.1539-6924.2010.01417.x
  • McCullagh P (1980) Regression Models for Ordinal Data. J Roy Stat Soc B 42(2):109–142. https://doi.org/10.1111/j.2517-6161.1980.tb01109.x
  • Muñiz C, Rodríguez P, Suárez MJ (2014) Sports and cultural habits by gender: an application using count-data models. Econ Model 36:288–297. https://doi.org/10.1016/j.econmod.2013.09.053
  • Oliveira-Brochado A, Quelhas Brito P, Oliveira-Brochado F (2017) Correlates of adults’ participation in sport and frequency of sport. Sci Sport 32(6):355–363. https://doi.org/10.1016/j.scispo.2017.03.005
  • Pohlmeier W, Ulrich V (1995) An econometric model of the two-part decision making process in the demand for health care. J Hum Resour 30(2):339–361. https://doi.org/10.2307/146123
  • Rhodes RE, Janssen I, Bredin SSD, Warburton DER, Bauman A (2017) Physical activity: health impact, prevalence, correlates and interventions. Psychol Health 32(8):942–975. https://doi.org/10.1080/08870446.2017.1325486
  • Sellers KF, Premeuax B (2021) Conway-Maxwell-Poisson regression models for dispersed count data. Wires Comput Stat 13(6):e1533. https://doi.org/10.1002/wics.1533
  • Slymen DJ, Ayala GX, Arredondo EM, Elder JP (2006) A demonstration of modelling count data with an application to physical activity. Epidemiol Perspect Innov 3:3. https://doi.org/10.1186/1742-5573-3-3
  • Sun J, Zhao X (2013) Analysis of panel count data. Springer, New York
  • Tang W, He H, Tu XM (2023) Applied categorical and count data analysis, 2nd edn. CRC Press, Boca Raton. https://doi.org/10.1201/9781003109815
  • WHO (2018) Global action plan on physical activity 2018–2030: More active people for a healthier world. https://apps.who.int/iris/bitstream/handle/10665/272722/9789241514187-eng.pdf?ua=1. Accessed 8 June 2023
  • Wilson P (2015) The misuse of the Vuong test for non-nested models to test for zero-inflation. Econ Lett 127:51–53. https://doi.org/10.1016/j.econlet.2014.12.029
  • Winkelmann R (2008) Econometric analysis of count data, 5th edn. Springer, Berlin. https://doi.org/10.1007/978-3-540-78389-3