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Unit Regression Models to Explain Vote Proportions in the Brazilian Presidential Elections in 2018
Blackbeta regression; Brazilian elections; Double-bounded variables;GAMLSS.
DOI:
https://doi.org/10.15446/rce.v47n2.111306Keywords:
Beta regression, simplex regression, Brazilian elections, double-bounded variables, GAMLSS (en)Elecciones brasileñas, GAMLSS, Regresión beta, Variables de doble límite (es)
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In this paper, we aim to identify the covariates associated with the proportion of votes of candidates elected in Brazilian municipalities with a population of more than 300,000 inhabitants. We analyzed the vote proportions from the 2018 presidential runoff election using distributions within the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) class. Unit distributions are quite useful for modeling vote proportions due to their flexibility to accommodate asymmetry and heavy tails. Furthermore, they provide adequate representations of the physiological properties and the empirical distribution of the data. We _t the beta, simplex, unit gamma, and unit Lindley regression models, considering random and fixed effects components to verify spatial correlation among the municipalities. The beta regression with fixed components regarding Brazilian regions is superior. The covariates with significant effects are the proportion of evangelicals, monthly household income per capita, the political spectrum of the governors' party elected in 2014 and 2018, and if the municipality is the capital of the state. We note that some Brazilian regions impact the vote proportions' mean and dispersion.
En este artículo, nuestro objetivo es identificar las covariables asociadas con la proporción de votos de los candidatos electos en municipios brasileños con una población de más de 300,000 habitantes. Analizamos las proporciones de votos de la segunda vuelta de las elecciones presidenciales de 2018 utilizando distribuciones dentro de la clase de Modelos Aditivos Generalizados para localización, Escala y Forma (GAMLSS). Las distribuciones unitarias son muy útiles para modelar proporciones de votos debido a su flexibilidad para acomodar asimetría y colas pesadas. Además, proporcionan representaciones adecuadas de las propiedades fisiológicas y la distribución empírica de los datos. Ajustamos los modelos de regresión beta, simplex, gamma unitario y Lindley, considerando componentes de efectos aleatorios y fijos para verificar la correlación espacial entre los municipios. La regresión beta con componentes fijos respecto a las regiones brasileñas es superior. Las covariables con efectos significativos son la proporción de evangélicos, el ingreso mensual por hogar per cápita, el espectro político del partido de los gobernadores elegidos en 2014 y 2018, y si el municipio es la capital del estado. Notamos que algunas regiones brasileñas impactan en la media y la dispersión de las proporciones de voto.
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