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

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  • Ronald McDonald
  • Xuxin Mao

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.

Suggested Citation

  • Ronald McDonald & Xuxin Mao, 2015. "Forecasting the 2015 General Election with Internet Big Data: An Application of the TRUST Framework," Working Papers 2016_03, Business School - Economics, University of Glasgow.
  • Handle: RePEc:gla:glaewp:2016_03
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    References listed on IDEAS

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    1. Ronald MacDonald & Xuxin Mao, 2015. "An Alternative way of Predicting the Outcome of the Scottish Independence Referendum: The Information in the Ether," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-69, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Alan S. Gerber & Dean Karlan & Daniel Bergan, 2009. "Does the Media Matter? A Field Experiment Measuring the Effect of Newspapers on Voting Behavior and Political Opinions," American Economic Journal: Applied Economics, American Economic Association, vol. 1(2), pages 35-52, April.
    3. Joakim Westerlund & David L. Edgerton, 2008. "A Simple Test for Cointegration in Dependent Panels with Structural Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(5), pages 665-704, October.
    4. Stefano DellaVigna & Ethan Kaplan, 2007. "The Fox News Effect: Media Bias and Voting," The Quarterly Journal of Economics, Oxford University Press, vol. 122(3), pages 1187-1234.
    5. Damiaan Persyn & Joakim Westerlund, 2008. "Error-correction–based cointegration tests for panel data," Stata Journal, StataCorp LP, vol. 8(2), pages 232-241, June.
    6. Huberty, Mark, 2015. "Can we vote with our tweet? On the perennial difficulty of election forecasting with social media," International Journal of Forecasting, Elsevier, vol. 31(3), pages 992-1007.
    7. Morwitz, Vicki G & Pluzinski, Carol, 1996. " Do Polls Reflect Opinions or Do Opinions Reflect Polls? The Impact of Political Polling on Voters' Expectations, Preferences, and Behavior," Journal of Consumer Research, Oxford University Press, vol. 23(1), pages 53-67, June.
    8. Ronald MacDonald & Xuxin Mao, "undated". "An Alternative way of predicting the putcome of the Scottish Independence Referendum: the information in the Ether," Working Papers 2015_05, Business School - Economics, University of Glasgow.
    9. Lebo, Matthew J. & Norpoth, Helmut, 2007. "The PM and the Pendulum: Dynamic Forecasting of British Elections," British Journal of Political Science, Cambridge University Press, vol. 37(01), pages 71-87, January.
    10. Whiteley, Paul, 1979. "Electoral Forecasting from Poll Data: The British Case," British Journal of Political Science, Cambridge University Press, vol. 9(02), pages 219-236, April.
    11. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
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