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Using Internet Data to Account for Special Events in Economic Forecasting

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  • Torsten Schmidt

    ()

  • Simeon Vosen

Abstract

Information about special events can improve economic forecasts substantially. However, due to the lack of timely quantitative data about these events, it has been difficult for professional forecasters to utilise such information in their forecasts. This paper investigates whether Internet search data can improve economic predictions in times of special events. An analysis of “cash for clunkers” programs in four selected countries exemplifies that including search query data into statistical forecasting models improves the forecasting performance in almost all cases. However, the challenge to identify irregular events and to find the appropriate time series from Google Insights for search remains.

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File URL: http://repec.rwi-essen.de/files/REP_12_382.pdf
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Bibliographic Info

Paper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen in its series Ruhr Economic Papers with number 0382.

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Length: 15 pages
Date of creation: Nov 2012
Date of revision:
Handle: RePEc:rwi:repape:0382

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Related research

Keywords: Forecast adjustment; Google Trends; private consumption;

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  1. Torsten Schmidt & Simeon Vosen, 2010. "A monthly consumption indicator for Germany based on internet search query data," Ruhr Economic Papers 0208, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  2. Graeme Chamberlin, 2010. "Googling the present," Economic and Labour Market Review, Palgrave Macmillan, vol. 4(12), pages 59-95, December.
  3. Askitas, Nikos & Zimmermann, Klaus F., 2009. "Google Econometrics and Unemployment Forecasting," IZA Discussion Papers 4201, Institute for the Study of Labor (IZA).
  4. Fildes, Robert & Stekler, Herman, 2002. "Reply to the comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 503-505, December.
  5. Jerome Adda & Russell Cooper, 1997. "Balladurette and Juppette: A Discrete Analysis of Scrapping Subsidies," NBER Working Papers 6048, National Bureau of Economic Research, Inc.
  6. Torsten Schmidt & Simeon Vosen, 2009. "Forecasting Private Consumption: Survey-based Indicators vs. Google Trends," Ruhr Economic Papers 0155, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  7. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
  8. D'Amuri, Francesco/FD & Marcucci, Juri/JM, 2009. ""Google it!" Forecasting the US unemployment rate with a Google job search index," MPRA Paper 18248, University Library of Munich, Germany.
  9. Goodwin, Paul, 2000. "Correct or combine? Mechanically integrating judgmental forecasts with statistical methods," International Journal of Forecasting, Elsevier, vol. 16(2), pages 261-275.
  10. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  11. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.
  12. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
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