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

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  • Levene, Mark
  • Fenner, Trevor

Abstract

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 2017 and 2019 UK general elections and to make predictions relating to the actual results 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.

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  • Levene, Mark & Fenner, Trevor, 2021. "A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1227-1234.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:3:p:1227-1234
    DOI: 10.1016/j.ijforecast.2021.02.002
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    References listed on IDEAS

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    1. Drew A. Linzer, 2013. "Dynamic Bayesian Forecasting of Presidential Elections in the States," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 124-134, March.
    2. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465, September.
    3. Hanretty, Chris & Lauderdale, Benjamin E. & Vivyan, Nick, 2018. "Comparing Strategies for Estimating Constituency Opinion from National Survey Samples," Political Science Research and Methods, Cambridge University Press, vol. 6(3), pages 571-591, July.
    4. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    5. Murr, Andreas E. & Stegmaier, Mary & Lewis-Beck, Michael S., 2021. "Vote Expectations Versus Vote Intentions: Rival Forecasting Strategies," British Journal of Political Science, Cambridge University Press, vol. 51(1), pages 60-67, January.
    6. Aleksejus Kononovicius, 2017. "Empirical Analysis and Agent-Based Modeling of the Lithuanian Parliamentary Elections," Complexity, Hindawi, vol. 2017, pages 1-15, November.
    7. Reade, J. James & Vaughan Williams, Leighton, 2019. "Polls to probabilities: Comparing prediction markets and opinion polls," International Journal of Forecasting, Elsevier, vol. 35(1), pages 336-350.
    8. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
    9. Mark Levene & Aleksejus Kononovicius, 2018. "Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting," Papers 1809.11052, arXiv.org, revised Jun 2019.
    10. Trevor Fenner & Mark Levene & George Loizou, 2016. "A stochastic evolutionary model generating a mixture of exponential distributions," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(2), pages 1-7, February.
    11. Trevor Fenner & Mark Levene & George Loizou, 2016. "A stochastic evolutionary model generating a mixture of exponential distributions," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(2), pages 1-7, February.
    12. Emanuele Taufer, 2007. "Modelling stylized features in default rates," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(1), pages 73-82, January.
    13. Jennings, Will & Lewis-Beck, Michael & Wlezien, Christopher, 2020. "Election forecasting: Too far out?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 949-962.
    14. Patrick Sturgis & Jouni Kuha & Nick Baker & Mario Callegaro & Stephen Fisher & Jane Green & Will Jennings & Benjamin E. Lauderdale & Patten Smith, 2018. "An assessment of the causes of the errors in the 2015 UK general election opinion polls," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 757-781, June.
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    2. Rytis Kazakevicius & Aleksejus Kononovicius & Bronislovas Kaulakys & Vygintas Gontis, 2021. "Understanding the nature of the long-range memory phenomenon in socioeconomic systems," Papers 2108.02506, arXiv.org, revised Aug 2021.

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