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    A stochastic differential equation approach to the analysis of the UK 2017 and 2019 general election polls

    Levene, Mark and Fenner, Trevor (2021) A stochastic differential equation approach to the analysis of the UK 2017 and 2019 general election polls. International Journal of Forecasting 37 (3), pp. 1227-1234. ISSN 0169-2070.

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    Human dynamics and sociophysics build on statistical models that can shed light on and add to our understanding of social phenomena. We propose a generative model based on a stochastic differential equation that enables us to model the opinion polls leading up to the UK 2017 and 2019 general elections, and to make predictions relating to the actual result of the elections. After a brief analysis of the time series of the poll results, we provide empirical evidence that the gamma distribution, which is often used in financial modelling, fits the marginal distribution of this time series. We demonstrate that the proposed poll-based forecasting model may improve upon predictions based solely on polls. The method uses the Euler-Maruyama method to simulate the time series, measuring the prediction error with the mean absolute error and the root mean square error, and as such could be used as part of a toolkit for forecasting elections.


    Item Type: Article
    Keyword(s) / Subject(s): election polls, forecasting elections, time series, stochastic differential equations, CIR process, gamma distribution, Euler-Maruyama method
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 15 Feb 2021 06:32
    Last Modified: 11 Jul 2021 15:17


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