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Using Internet Data to Account for Special Events in Economic Forecasting

  • Torsten Schmidt


  • Simeon Vosen

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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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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  1. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
  2. 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.
  3. Paul Goodwin, 2005. "How to Integrate Management Judgment with Statistical Forecasts," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 1, pages 8-12, June.
  4. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
  5. Graeme Chamberlin, 2010. "Googling the present," Economic and Labour Market Review, Palgrave Macmillan, vol. 4(12), pages 59-95, December.
  6. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 683-687.
  7. 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.
  8. Jerome Adda & Russell Cooper, 2000. "Balladurette and Juppette: A Discrete Analysis of Scrapping Subsidies," Journal of Political Economy, University of Chicago Press, vol. 108(4), pages 778-806, August.
  9. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  10. 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.
  11. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
  12. Goodwin, Paul, 2000. "Correct or combine? Mechanically integrating judgmental forecasts with statistical methods," International Journal of Forecasting, Elsevier, vol. 16(2), pages 261-275.
  13. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
  14. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.
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