Blomqvist, BRH, Sumpter, DJT and Mann, RP orcid.org/0000-0003-0701-1274 (2019) Inferring the dynamics of rising radical right-wing party support using Gaussian processes. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 377 (2160). ISSN 1364-503X
Abstract
The use of classical regression techniques in social science can prevent the discovery of complex, nonlinear mechanisms, and often relies too heavily on both the expertise and prior expectations of the data analyst. In this paper, we present a regression methodology that combines the interpretability of traditional, well used, statistical methods with the full predictability and flexibility of Bayesian statistics techniques. Our modelling approach allow us to find and explain the mechanisms behind the rise of Radical Right-wing Populist parties (RRPs), that we would have been unable to find using traditional methods. Using Swedish municipality level data (2002-2018) we find no evidence that the proportion of foreignborn residents is predictive of increases in RRP support. Instead, education levels and population density are the significant variables that impact the change in support for the RRP, in addition to spatial and temporal control variables. We argue that our methodology, which produces models with considerably better fit of the complexity and nonlinearities often found in social systems, provides a better tool for hypothesis testing and exploration of theories about RRPs and other social movements.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Author(s). Published by the Royal Society. All rights reserved.This manuscript version is made available under the CC BY license [https://creativecommons.org/licenses/by/4.0/]. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Gaussian processes, Coupling functions, Radical right-wing parties, Bayesian statistics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Funding Information: | Funder Grant number Alan Turing Institute Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Oct 2019 15:15 |
Last Modified: | 15 Nov 2019 12:57 |
Status: | Published |
Publisher: | The Royal Society |
Identification Number: | 10.1098/rsta.2019.0145 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151456 |