Abstract
A Bayesian hierarchical model is proposed to forecast outcomes of binary referenda based on opinion poll data acquired over a period of time. It is demonstrated how the model provides a consistent probabilistic prediction of the final outcomes over the preceding months, effectively smoothing the volatility exhibited by individual polls. The method is illustrated using opinion poll data published before the Scottish independence referendum in 2014, in which Scotland voted to remain a part of the United Kingdom, and subsequently validate it on the data related to the 2016 referendum on the continuing membership of the United Kingdom in the European Union.
Highlights
Opinion polls provide predictions of the outcomes of voting events such as elections or referenda, based on samples drawn from the population that is eligible to vote
Forecasting from opinion polls can be of widespread interest: to the public, to the media, to those running campaigns, or to individuals standing for election
Similar operationalisation has been adopted for the EU membership referendum, where s0 relates to the share of the Leave votes, favouring the withdrawal of the UK from the European Union
Summary
Opinion polls provide predictions of the outcomes of voting events such as elections or referenda, based on samples drawn from the population that is eligible to vote. The forecasts can aid decision making, for example on the amount of money spent on campaigning, by providing a description of uncertainty about the outcome of the voting event. J.J. Forster et al / Computational Statistics and Data Analysis 133 (2019) 90–103 described and analysed, with detailed outcomes presented for the Scottish referendum, and the EU membership referendum used for external model validation.
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