Forecasting multiparty by-elections using Dirichlet regression

Article
Elections
Britain
Author

Chris Hanretty

Published

October 1, 2021

Model performance over next ten by-elections, for models estimated on progressively larger windows of data from 1945 onwards

Abstract

By-elections, or special elections, play an important role in many democracies – but whilst there are multiple forecasting models for national elections, there are no such models for multiparty by-elections. Multiparty by-elections present particular analytic problems related to the compositional character of the data and structural zeros where parties fail to stand. I model party vote shares using Dirichlet regression, a technique suited for compositional data analysis. After identifying predictor variables from a broader set of candidate variables, I estimate a Dirichlet regression model using data from all post-war by-elections in the UK (n=468). The cross-validated error of the model is comparable to the error of costly and infrequent by-election polls (MAE: 4.0 compared to 3.6 for polls). The steps taken in the analysis are in principle applicable to any system that uses by-elections to fill legislative vacancies.

Full-text

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Citation

Hanretty, Chris. 2021. “Forecasting Multiparty by-Elections Using Dirichlet Regression.” International Journal of Forecasting 37 (4): 1666–76. https://doi.org/10.1016/j.ijforecast.2021.03.007.