Count Data Regression: Modeling Diversification in Sports Participation in Spain

Jaume García (), Cristina Muñiz () and María José Suárez ()
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Jaume García: Universitat Pompeu Fabra
Cristina Muñiz: Universidad de Oviedo
María José Suárez: Universidad de Oviedo

Chapter Chapter 1 in Applied Econometric Analysis Using Cross Section and Panel Data, 2023, pp 3-32 from Springer

Abstract: Abstract 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.

Keywords: Overdispersion; Count variables; Excess of zeros; Zero-inflated models; Hurdle models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-981-99-4902-1_1

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DOI: 10.1007/978-981-99-4902-1_1

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