Forecasting the 2015 General Election with Internet Big Data: An Application of the TRUST Framework

Ronald McDonald and Xuxin Mao
Authors registered in the RePEc Author Service: Ronald MacDonald

Working Papers from Business School - Economics, University of Glasgow

Abstract: Many variables, such as currencies, are very difficult to predict and often researchers demonstrate that a simple random walk process can out-perform a model-based forecast using fundamentals. However, there is increasing evidence that such results can be overturned with the use of rich enough dynamic processes in the underlying statistical modelling and also by ensuring that a rich enough information set is used. Elections have also become increasingly difficult to predict, despite the use of increasingly sophisticated methods, with the 2015 UK General Election being a good case in point. In this paper we demonstrate that the kind of statistical methods used to predict currencies and other financial variables, combined with information culled from internet sources such as Google trends, can greatly improve on the predictions based solely on opinion polls. This paper offers the first real time test of the so-called Big Data for the UK 2015 General Election. Our real time predictions of both the overall UK and Scottish components of the election are very close to the actual outcomes.

Date: 2015-10
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Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2016_03

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