Forecasting elections in Europe: Synthetic models
Forecasting elections in Europe: Synthetic models
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预测欧洲选举：综合模型
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Ruth Dassonneville
中科院分区：
文献类型：
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作者：
M. Lewis;Ruth Dassonneville
以美国为例，关于全国大选预测的科学工作已经发展得最成熟，可以确定三种主要的方法：结构主义者、聚集者和合成者。对于欧洲的情况，选举预测模型几乎完全是结构主义的。在这里，我们将结构建模和聚合投票结果结合在一起，形成一个混合模型，我们将其命名为合成模型。这个模型包含一个政治经济学核心，在这个核心上添加了民调数据(以利用被省略的变量)。我们将这个模型应用于三个西欧国家的样本：德国、爱尔兰和英国。就领先优势和准确性而言，这种组合策略似乎提供了明显的预测收益。
Scientific work on national election forecasting has become most developed for the United States case, where three dominant approaches can be identified: Structuralists, Aggregators, and Synthesizers. For European cases, election forecasting models remain almost exclusively Structuralist. Here we join together structural modeling and aggregate polling results, to form a hybrid, which we label a Synthetic Model. This model contains a political economy core, to which poll numbers are added (to tap omitted variables). We apply this model to a sample of three Western European countries: Germany, Ireland, and the United Kingdom. This combinatory strategy appears to offer clear forecasting gains, in terms of lead and accuracy.