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Using Polls to Forecast Popular Vote Share for US Presidential Elections 2016 and 2020: An Optimal Forecast Combination Based on Ensemble Empirical Model

Author

Listed:
  • Easaw, Joshy

    (Cardiff Business School)

  • Fang, Yongmei

    (College of Mathematics and Informatics, South China Agricultural University, China)

  • Heravi, Saeed

    (Cardiff Business School)

Abstract

This study introduces the Ensemble Empirical Mode Decomposition (EEMD) technique to forecasting popular vote share. The technique is useful when using polling data, which is pertinent when none of the main candidates is the incumbent. Our main interest in this study is the short- and long-term forecasting and, thus, we consider from the short forecast horizon of 1-day to three months ahead. The EEMD technique is used to decompose the election data for the two most recent US presidential elections; 2016 and 2020 US. Three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are then used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model are compared with the benchmark individual models, SVM, NN, and ARIMA. In addition, this compared to the single prediction market IOWA Electronic Markets. The results indicated that the prediction performance of EEMD combined model is better than that of individual models.

Suggested Citation

  • Easaw, Joshy & Fang, Yongmei & Heravi, Saeed, 2021. "Using Polls to Forecast Popular Vote Share for US Presidential Elections 2016 and 2020: An Optimal Forecast Combination Based on Ensemble Empirical Model," Cardiff Economics Working Papers E2021/34, Cardiff University, Cardiff Business School, Economics Section.
    • Handle: RePEc:cdf:wpaper:2021/34
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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. Lauderdale, Benjamin E. & Linzer, Drew, 2015. "Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting," International Journal of Forecasting, Elsevier, vol. 31(3), pages 965-979.
    3. Yongmei Fang & Bo Guan & Shangjuan Wu & Saeed Heravi, 2020. "Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 877-886, September.
    4. Graefe, Andreas & Küchenhoff, Helmut & Stierle, Veronika & Riedl, Bernhard, 2015. "Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems," International Journal of Forecasting, Elsevier, vol. 31(3), pages 943-951.
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    More about this item

    Keywords

    Forecasting Popular Votes Shares; Electoral Poll; Forecast combination; Hybrid model; Support Vector Machine;
    All these keywords.

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