ABSTRACT

The vote shares by party on a given subdivision of a territory form a vector called composition (mathematically, a vector belonging to a simplex). It is interesting to model these shares and study the impact of the characteristics of the territorial units on the outcome of the elections. In the political economy literature, few regression models are adapted to the case of more than two political parties. In the statistical literature, there are regression models adapted to share vectors including Compositional Data (CoDa) models, but also Dirichlet models, and others. Our goal is to discuss and illustrate the use CoDa regression models for political economy models for more than two parties. The models are fitted on French electoral data of the 2015 departmental elections.

摘要

政党在给定的地区细分中的​​投票份额由一个称为“成分”的向量（数学上是属于单纯形的向量）组成。对这些份额进行建模并研究领土单位的特征对选举结果的影响是很有趣的。在政治经济学文献中，很少有回归模型适用于两个以上政党的情况。在统计文献中，存在适用于共享向量的回归模型，包括成分数据（CoDa）模型，还有Dirichlet模型等。我们的目标是讨论和说明在超过两个政党的政治经济学模型中使用CoDa回归模型。这些模型基于2015年部门选举的法国选举数据进行拟合。