Replication data for: A Fast, Easy, & Efficient Estimator for Multiparty Electoral Data

Katz and King have previously developed a model for predicting or explaining aggregate electoral results in multiparty democracies. Their model is, in principle, analogous to what least-squares regression provides American political researchers in that two-party system. Katz and King applied their model to three-party elections in England and revealed a variety of new features of incumbency advantage and sources of party support. Although the mathematics of their statistical model covers any number of political parties, it is computationally demanding, and hence slow and numerically imprecise, with more than three parties. In this paper we produce an approximate method that works in practice with many parties without making too many theoretical compromises. Our approach is to treat the problem as one of missing data. This allows us to use a modification of the fast EMis algorithm of King, Honaker, Joseph, and Scheve and to provide easy-to-use software, while retaining the attractive features of the Katz and King model, such as the t distribution and explicit models for uncontested seats.

Katz与King此前已开发出一款用于预测或解释多党制民主政体下整体选举结果的模型。该模型的原理与两党制语境下最小二乘回归（least-squares regression）为美国政治研究者提供的分析范式相仿。Katz与King将该模型应用于英格兰的三党选举场景，揭示了现任者优势与政党支持来源的诸多新特征。尽管其统计模型的数学框架可覆盖任意数量的政党，但当政党数量超过三个时，该模型存在计算负荷过大的问题，进而导致运算速度缓慢且数值精度不足。本文提出一种近似方法，该方法在实际应用中可适配多党场景，且无需做出过多理论妥协。我们的研究路径是将该问题视作缺失数据问题，这一处理方式使我们得以对King、Honaker、Joseph与Scheve提出的快速EMis算法（fast EMis algorithm）进行改进，并在保留Katz与King模型优良特性的前提下推出易于使用的软件工具——这些特性包括t分布（t distribution）以及针对无竞争席位的显式建模。