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Predicting bloc support in Irish general elections 1951–2020: A political history model
[journal article]
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 individu... view more
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.... view less
Keywords
prognosis; political history; structural model; party; election; Ireland; election result; predictive model
Classification
Political Process, Elections, Political Sociology, Political Culture
Free Keywords
Forecasting
Document language
English
Publication Year
2022
Page/Pages
p. 1-22
Journal
Journal of Elections, Public Opinion and Parties (2022)
DOI
https://doi.org/10.1080/17457289.2022.2120884
ISSN
1745-7297
Status
Published Version; peer reviewed
Licence
Creative Commons - Attribution 4.0
FundingGefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491156185 / Funded by the German Research Foundation (DFG) - Project number 491156185