Journal article
Forecasting government support in Irish general elections: Opinion polls and structural models
International journal of forecasting, Vol.37(4), pp.1654-1665
06/2021
DOI: 10.1016/j.ijforecast.2021.03.006
Abstract
Election forecasting is a cottage industry among pollsters, the media, political scientists, and political anoraks. Here, we plow a fresh field in providing a systematic exploration of election forecasting in Ireland. We develop a structural forecast model for predicting incumbent government support in Irish general elections between 1977 and 2020 (the Iowa model). We contrast this structural model with forecasts from opinion polls, the dominant means of predicting Ireland’s elections to date. Our results show that with appropriate lead-in time, structural models perform similarly to opinion polls in predicting government support when the dependent variable is vote share. Most importantly, however, the Iowa model is superior to opinion polls in predicting government seat share, the ultimate decider of government fate in parliamentary systems, and especially significant in single transferable vote (STV) systems where vote and seat shares are not always in sync. Our results provide cumulative evidence of the potency of structural electoral forecast models globally, with the takeaway that the Iowa model estimating seat share outpaces other prediction approaches in anticipating government performance in Irish general elections.
Details
- Title: Subtitle
- Forecasting government support in Irish general elections: Opinion polls and structural models
- Creators
- Stephen Quinlan - GESIS Leibniz Institut fur Sozialwissenschaften eV, Mannheim, Baden Wuerttemberg, GermanyMichael S Lewis-Beck - University of Iowa, IA 52242, USA
- Resource Type
- Journal article
- Publication Details
- International journal of forecasting, Vol.37(4), pp.1654-1665
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.ijforecast.2021.03.006
- ISSN
- 0169-2070
- eISSN
- 1872-8200
- Language
- English
- Date published
- 06/2021
- Academic Unit
- Political Science
- Record Identifier
- 9984090792702771
Metrics
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