Journal article
Predicting bloc support in Irish general elections 1951-2020: A political history model
Journal of elections, public opinion and parties, Vol.34(1), pp.136-157
01/02/2024
DOI: 10.1080/17457289.2022.2120884
Abstract
Election forecasting is a growing enterprise. Structural models relying on "fundamental" political and economic variables, principally to predict government performance, are popular in political science. Conventional wisdom though is these standard structural models fall short in predicting individual blocs' performance and their applicability to multiparty systems is restricted. We challenge this by providing a structural forecast of bloc performance in Ireland, a case primarily overlooked in the election forecasting literature. Our model spurns the economic and performance variables conventionally associated with structural forecasting enterprises and instead concentrates on Ireland's historical party and governance dynamics in the vein of testing whether these patterns alone offer solid predictions of election outcomes. Using Seemingly Unrelated Regression (SUR), our approach, comprising measures of incumbency, short-term party support, and political and economic shocks, offers reasonable predictions of the vote share performance of four blocs: Ireland's two major parties, Fianna Fáil and Fine Gael, Independents, and the Left bloc combined across 20 elections spanning 60 years.
Details
- Title: Subtitle
- Predicting bloc support in Irish general elections 1951-2020: A political history model
- Creators
- Stephen Quinlan - GESIS - Leibniz Institute for the Social SciencesMichael S. Lewis-Beck - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of elections, public opinion and parties, Vol.34(1), pp.136-157
- DOI
- 10.1080/17457289.2022.2120884
- ISSN
- 1745-7289
- eISSN
- 1745-7297
- Publisher
- Routledge
- Language
- English
- Electronic publication date
- 10/21/2022
- Date published
- 01/02/2024
- Academic Unit
- Political Science
- Record Identifier
- 9984315589802771
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