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Estimator Comparison for the Prediction of Election Results

Author

Listed:
  • Miltiadis S. Chalikias

    (Department of Accounting and Finance, University of West Attica, 12244 Egaleo, Greece)

  • Georgios X. Papageorgiou

    (Department of Accounting and Finance, University of West Attica, 12244 Egaleo, Greece)

  • Dimitrios P. Zarogiannis

    (Department of Accounting and Finance, University of West Attica, 12244 Egaleo, Greece)

Abstract

Cluster randomized experiments and estimator comparisons are well-documented topics. In this paper, using the datasets of the popular vote in the presidential elections of the United States of America (2012, 2016, 2020), we evaluate the properties (SE, MSE) of three cluster sampling estimators: Ratio estimator, Horvitz–Thompson estimator and the linear regression estimator. While both the Ratio and Horvitz–Thompson estimators are widely used in cluster analysis, we propose a linear regression estimator defined for unequal cluster sizes, which, in many scenarios, performs better than the other two. The main objective of this paper is twofold. Firstly, to indicate which estimator is most suited for predicting the outcome of the popular vote in the United States of America. We do so by applying the single-stage cluster sampling technique to our data. In the first partition, we use the 50 states plus the District of Columbia as primary sampling units, whereas in the second one, we use 3112 counties instead. Secondly, based on the results of the aforementioned procedure, we estimate the number of clusters in a sample for a set standard error while also considering the diminishing returns from increasing the number of clusters in the sample. The linear regression estimator is best in the majority of the examined cases. This type of comparison can also be used for the estimation of any other country’s elections if prior voting results are available.

Suggested Citation

  • Miltiadis S. Chalikias & Georgios X. Papageorgiou & Dimitrios P. Zarogiannis, 2024. "Estimator Comparison for the Prediction of Election Results," Stats, MDPI, vol. 7(3), pages 1-14, July.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:3:p:40-684:d:1427053
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    References listed on IDEAS

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    1. Green, Donald P. & Vavreck, Lynn, 2008. "Analysis of Cluster-Randomized Experiments: A Comparison of Alternative Estimation Approaches," Political Analysis, Cambridge University Press, vol. 16(2), pages 138-152, April.
    2. Arceneaux, Kevin & Nickerson, David W., 2009. "Modeling Certainty with Clustered Data: A Comparison of Methods," Political Analysis, Cambridge University Press, vol. 17(2), pages 177-190, April.
    3. Andreas Graefe, 2018. "Predicting elections: Experts, polls, and fundamentals," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(4), pages 334-344, July.
    4. Esarey, Justin & Menger, Andrew, 2019. "Practical and Effective Approaches to Dealing With Clustered Data," Political Science Research and Methods, Cambridge University Press, vol. 7(3), pages 541-559, July.
    5. Katz, Jonathan N. & King, Gary, 1999. "A Statistical Model for Multiparty Electoral Data," American Political Science Review, Cambridge University Press, vol. 93(1), pages 15-32, March.
    6. Kevin Arceneaux, 2005. "Using Cluster Randomized Field Experiments to Study Voting Behavior," The ANNALS of the American Academy of Political and Social Science, , vol. 601(1), pages 169-179, September.
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