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Modelling abstention rate using spatial regression
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Autores
Orientador(es)
Resumo(s)
During the last few elections that were held in Portugal, there have been very low
percentages of voter turnout. This will obviously impact the result of those elections and can
maybe be related to the general disenchantment of the population regarding the country’s
recent political environment.
This study aims to contribute to a better understanding of the patterns in the abstention
rate of the last elections in Portugal. Sociological and economic variables such as age,
unemployment rate, education level and many others will be used in trying to find out if
they influence the abstention rate. It is logical to assume that the abstention rate in a certain
municipality will be related to the abstention in neighboring municipalities. Therefore, the
study also investigates if there is spatial autocorrelation in the abstention rates.
Modeling a phenomenon like this with a simple linear regression model, estimated by
Ordinary Least Squares (OLS), will render less efficient and biased results because of the
spatial correlation of the observations and possible spatial clustering of values. Spatial
regression methods have been proposed to overcome these drawbacks, particularly the Geographically Weighted Regression (GWR). This method will take into account possible
local influences, allowing the coefficients of the model to vary depending on the geographic
location, possibly obtaining a more appropriate fit. Many different OLS and GWR models
were investigated by considering different combinations of explanatory variables and
diagnosing their results through statistical tests and goodness-of-fit measures.
Results show that indeed the data exhibits a non-random spatial pattern, and that a GWR
model is a better approach in modeling abstention rates, when compared to an OLS model.
Hence, the percentage of voter turnout in a municipality is likely to be better modelled
taking into account its geographic location.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Palavras-chave
Voter turnout Abstention rate Sociological Variables Economic Variables Spatial Analysis Geographically Weighted Regression Spatial Non-Stationarity