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Election forecasting: Too far out?

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  • Jennings, Will
  • Lewis-Beck, Michael
  • Wlezien, Christopher

Abstract

We consider two criteria for evaluating election forecasts: accuracy (precision) and lead (distance from the event), specifically the trade-off between the two in poll-based forecasts. We evaluate how much “lead” still allows prediction of the election outcome. How much further back can we go, supposing we tolerate a little more error? Our analysis offers estimates of the “optimal” lead time for election forecasts, based on a dataset of over 26,000 vote intention polls from 338 elections in 44 countries between 1942 and 2014. We find that optimization of a forecast is possible, and typically occurs two to three months before the election, but can be influenced by the arrangement of political institutions. To demonstrate how our optimization guidelines perform in practice, we consider recent elections in the UK, the US, and France.

Suggested Citation

  • Jennings, Will & Lewis-Beck, Michael & Wlezien, Christopher, 2020. "Election forecasting: Too far out?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 949-962.
  • Handle: RePEc:eee:intfor:v:36:y:2020:i:3:p:949-962
    DOI: 10.1016/j.ijforecast.2019.12.002
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    References listed on IDEAS

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    1. Will Jennings & Christopher Wlezien, 2018. "Election polling errors across time and space," Nature Human Behaviour, Nature, vol. 2(4), pages 276-283, April.
    2. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, November.
    3. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    4. James Honaker & Gary King, 2010. "What to Do about Missing Values in Time‐Series Cross‐Section Data," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 561-581, April.
    5. Christopher Wlezien & Will Jennings & Stephen Fisher & Robert Ford & Mark Pickup, 2013. "Polls and the Vote in B ritain," Political Studies, Political Studies Association, vol. 61, pages 129-154, April.
    6. Michael Lopp, 2016. "1.0," Springer Books, in: Managing Humans, edition 3, chapter 0, pages 133-141, Springer.
    7. Will Jennings & Christopher Wlezien, 2016. "The Timeline of Elections: A Comparative Perspective," American Journal of Political Science, John Wiley & Sons, vol. 60(1), pages 219-233, January.
    8. Lall, Ranjit, 2016. "How Multiple Imputation Makes a Difference," Political Analysis, Cambridge University Press, vol. 24(4), pages 414-433.
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    Cited by:

    1. Hanretty, Chris, 2021. "Forecasting multiparty by-elections using Dirichlet regression," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1666-1676.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. 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.

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