The Library
Citizen forecasts of the 2021 German election
Tools
Tools
Tools
Murr, Andreas and Lewis-Beck, Michael S. (2022) Citizen forecasts of the 2021 German election. PS: Political Science & Politics, 55 (1). pp. 97-101. doi:10.1017/S1049096521000925 ISSN 1049-0965.
|
PDF
WRAP-Citizen-forecasts-2021-German-election-2021.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (723Kb) | Preview |
Official URL: https://doi.org/10.1017/S1049096521000925
Abstract
There are various scientific approaches to election forecasting: poll aggregation, structural models, electronic markets, and citizen forecasting. With respect to the German case, the first two approaches—polls and models—perhaps have been the most popular. However, relatively little work has been done deploying citizen forecasting (CF), the approach described in this article. In principle, CF differs considerably from other methods and appears, on its face, quite simple. Before an election, citizens are asked in a national survey who they think will win. As the percentage of expectations for party X increases, the likelihood of an X win is judged to be higher. The method has been applied regularly with success in other established democracies, such as the United Kingdom and the United States.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | J Political Science > JN Political institutions (Europe) | ||||||||
| Divisions: | Faculty of Social Sciences > Politics and International Studies | ||||||||
| Library of Congress Subject Headings (LCSH): | Elections -- Germany (West) -- Public opinion, Election forecasting -- Germany (West), Elections -- Germany (West), Public opinion -- Germany (West), Germany (West) -- Politics and government -- Public opinion, Elections -- Germany -- History -- 21st century | ||||||||
| Journal or Publication Title: | PS: Political Science & Politics | ||||||||
| Publisher: | Cambridge University Press | ||||||||
| ISSN: | 1049-0965 | ||||||||
| Official Date: | January 2022 | ||||||||
| Dates: |
|
||||||||
| Volume: | 55 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 97-101 | ||||||||
| DOI: | 10.1017/S1049096521000925 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | This article has been accepted for publication in a revised form for publication in PS: Political Science & Politics https://www.cambridge.org/core/journals/ps-political-science-and-politics | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Copyright Holders: | © The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association | ||||||||
| Date of first compliant deposit: | 21 June 2021 | ||||||||
| Date of first compliant Open Access: | 22 June 2021 | ||||||||
| Version or Related Resource: | Replicaiton code and data (submitted for review): Murr, Andreas E., and Michael S Lewis-Beck. 2021. “Replication Archive for: Citizen Forecasts of the 2021 German Election” Harvard Dataverse. doi: 10.7910/DVN/WVTI2K | ||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
