Predicting the Brexit outcome using singular spectrum analysis

In a referendum conducted in the United Kingdom (UK) on June 23, 2016, $51.6\\%$ of the participants voted to leave the European Union (EU). The outcome of this referendum had major policy and financial impact for both UK and EU, and was seen as a surprise because the predictions consistently indicate that the ``Remain'''' would get a majority. In this paper, we investigate whether the outcome of the Brexit referendum could have been predictable by polls data. The data consists of 233 polls which have been conducted between January 2014 and June 2016 by YouGov, Populus, ComRes, Opinion, and others. The sample size range from 500 to 20058. We used Singular Spectrum Analysis (SSA) which is an increasingly popular and widely adopted filtering technique for both short and long time series. We found that the real outcome of the referendum is very close to our point estimate and within our prediction interval, which reinforces the usefulness of SSA to predict polls data.

Keywords:

Singular Spectrum Analysis , Recurrent SSA forecasting algorithm , Polls data