Forecasting the party support in Estonia: comparison of machine learning regression algorithms
Date
2018
Authors
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Publisher
Tartu Ülikool
Abstract
Forecasting political behavior using economic indicators is not a very new phenomenon with
the earliest literature going back as far as the 1930s. In the present day, there exists a lot of
research on the topic, but the majority of these studies have been conducted in the context
of a very limited number of countries such as the United States or the Western European
ones. By comparison, the research on forecasting the political behavior using economic
voting in Estonia is almost non-existent. This thesis will be the first in-depth study conducted
at that level and forecasts the party support of the Estonian Reform Party and the Estonian
Center Party using economic indicators as the predictor variables. Based on the previous
economic voting theory, it has been argued that the theoretically correct model to forecast
using these variables is the linear regression due to the expected associations between the
economic variables and party support. However, this thesis contests this claim and argues
that when analyzing the phenomena of forecasting party support using economic indicators,
certain modern machine learning algorithms could be considered as legitimate alternatives to
the linear regression, as each of them addresses the different shortcomings of the model. For
this reason, this thesis compares the methods of linear regression, regularized linear models,
autoregressive integrated moving average, and the decision-tree models to see whether the
more modern approaches are able to improve upon the default linear regression model.