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Polls, Context, and Time: A Dynamic Hierarchical Bayesian Forecasting Model for US Senate Elections

Published online by Cambridge University Press:  18 January 2022

Yehu Chen ,
Roman Garnett  and
Jacob M. Montgomery Open the ORCID record for Jacob M. Montgomery [Opens in a new window]
Yehu Chen
Affiliation:
Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA. E-mail: chenyehu@wustl.edu
Roman Garnett
Affiliation:
Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA. E-mail: garnett@wustl.edu
Jacob M. Montgomery*
Affiliation:
Department of Political Science, Washington University in St. Louis, St. Louis, MO, USA. E-mail: jacob.montgomery@wustl.edu
*
Corresponding author Jacob M. Montgomery
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Abstract

We present a hierarchical Dirichlet regression model with Gaussian process priors that enables accurate and well-calibrated forecasts for U.S. Senate elections at varying time horizons. This Bayesian model provides a balance between predictions based on time-dependent opinion polls and those made based on fundamentals. It also provides uncertainty estimates that arise naturally from historical data on elections and polls. Experiments show that our model is highly accurate and has a well calibrated coverage rate for vote share predictions at various forecasting horizons. We validate the model with a retrospective forecast of the 2018 cycle as well as a true out-of-sample forecast for 2020. We show that our approach achieves state-of-the art accuracy and coverage despite relying on few covariates.

Keywords

forecastsBayesianGaussian processelections
Type
Article
Information
Political Analysis , Volume 31 , Issue 1 , January 2023 , pp. 113 - 133
Copyright
© The Author(s) 2022. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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Chen et al. Dataset

Dataset

https://doi.org/10.7910/DVN/GNHESM
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