首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Bayesian forecasting of electoral outcomes with new parties’ competition
Institution:1. ICREA-A & Department of Economics and Business (UPF) and Barcelona Graduate School of Economics, Spain;2. Warwick University, UK;1. Wake Forest University, USA;2. Saint Louis University, USA;1. Paris School of Business, 59 Rue Nationale, 75013, Paris, France;2. University of York, UK;1. University of Strasbourg, CNRS, BETA, France;2. TIMAS, Thang Long University, Viet Nam;3. FERDI, Clermont Ferrand, France;1. Département des Sciences Économiques, ESG-UQAM, Montréal, Canada;2. CESifo, Germany;3. CORE, Belgium;4. Universidad de los Andes, School of Government, Colombia;5. CORE, Université de Louvain, Belgium;6. CREPP, Université de Liège, Belgium;7. Toulouse School of Economics, France
Abstract:We propose a new methodology for predicting electoral results that combines a fundamental model and national polls within an evidence synthesis framework. Although novel, the methodology builds upon basic statistical structures, largely modern analysis of variance type models, and it is carried out in open-source software. The methodology is motivated by the specific challenges of forecasting elections with the participation of new political parties, which is becoming increasingly common in the post-2008 European panorama. Our methodology is also particularly useful for the allocation of parliamentary seats, since the vast majority of available opinion polls predict at national level whereas seats are allocated at local level. We illustrate the advantages of our approach relative to recent competing approaches using the 2015 Spanish Congressional Election. In general, the predictions of our model outperform the alternative specifications, including hybrid models that combine fundamental and polls models. Our forecasts are, in relative terms, particularly accurate in predicting the seats obtained by each political party.
Keywords:Multilevel models  Bayesian machine learning  Inverse regression  Evidence synthesis  Elections
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号