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Estimating coalition majorities during political campaigns based on pre-election polls


Stoetzer, Lukas F; Orlowski, Matthias (2020). Estimating coalition majorities during political campaigns based on pre-election polls. Journal of Elections, Public Opinion, and Parties, 30(1):126-137.

Abstract

In multi-party systems, politicians, voters, and political pundits often speculate about potential coalition governments based on current poll results. Their interest particularly centers around the question whether a specific coalition has enough public support to form a parliamentary majority. In this research note, we present a Bayesian Dynamic Multinomial-Dirichlet model to estimate the probability that a coalition will find enough public support to form a parliamentary majority. An application to German federal elections from 1994–2017 and comparisons with alternative methods underscore the value of this approach.

Abstract

In multi-party systems, politicians, voters, and political pundits often speculate about potential coalition governments based on current poll results. Their interest particularly centers around the question whether a specific coalition has enough public support to form a parliamentary majority. In this research note, we present a Bayesian Dynamic Multinomial-Dirichlet model to estimate the probability that a coalition will find enough public support to form a parliamentary majority. An application to German federal elections from 1994–2017 and comparisons with alternative methods underscore the value of this approach.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
Dewey Decimal Classification:320 Political science
Scopus Subject Areas:Social Sciences & Humanities > Sociology and Political Science
Uncontrolled Keywords:Sociology and Political Science
Language:English
Date:February 2020
Deposited On:01 Oct 2020 11:36
Last Modified:02 Oct 2020 20:00
Publisher:Taylor & Francis
ISSN:1745-7289
OA Status:Closed
Publisher DOI:https://doi.org/10.1080/17457289.2019.1582533

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