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Forecasting Private Consumption: Survey-based Indicators vs. Google Trends

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  • Torsten Schmidt
  • Simeon Vosen

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Abstract

In this study we introduce a new indicator for private consumption based on search query time series provided by Google Trends. The indicator is based on factors extracted from consumption-related search categories of the Google Trends application Insights for Search. The forecasting performance of the new indicator is assessed relative to the two most common survey-based indicators - the University of Michigan Consumer Sentiment Index and the Conference Board Consumer Confidence Index. The results show that in almost all conducted in-sample and out-of-sample forecasting experiments the Google indicator outperforms the survey-based indicators. This suggests that incorporating information from Google Trends may off er signifi cant benefi ts to forecasters of private consumption.

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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 0155.

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Length: 23 pages
Date of creation: Nov 2009
Date of revision:
Handle: RePEc:rwi:repape:0155

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Keywords: Google Trends; private consumption; forecasting; Consumer Sentiment Indicator;

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References

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  1. Jason Bram & Sydney Ludvigson, 1998. "Does consumer confidence forecast household expenditure? a sentiment index horse race," Economic Policy Review, Federal Reserve Bank of New York, issue Jun, pages 59-78.
  2. 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.
  3. Carroll, Christopher D & Fuhrer, Jeffrey C & Wilcox, David W, 1994. "Does Consumer Sentiment Forecast Household Spending? If So, Why?," American Economic Review, American Economic Association, vol. 84(5), pages 1397-1408, December.
  4. Croushore, Dean, 2005. "Do consumer-confidence indexes help forecast consumer spending in real time?," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 435-450, December.
  5. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  6. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
  7. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  8. James A Wilcox, 2007. "Forecasting Components of Consumption with Components of Consumer Sentiment," Business Economics, Palgrave Macmillan, vol. 42(4), pages 22-32, October.
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Citations

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Cited by:
  1. Frédéric Karamé & Yannick Fondeur, 2012. "Can Google Data Help Predict French Youth Unemployment?," Documents de recherche 12-03, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
  2. Roland Döhrn, 2011. "Konjunkturprognosen in bewegten Zeiten: Die Kunst des Unmöglichen?," RWI Materialien, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 26, 01.
  3. Yan Carrière‐Swallow & Felipe Labbé, 2013. "Nowcasting with Google Trends in an Emerging Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 289-298, 07.
  4. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
  5. Torsten Schmidt & Simeon Vosen, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 0382, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  6. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
  7. Roland Döhrn & Christoph M. Schmidt, 2010. "Information or Institution? – On the Determinants of Forecast Accuracy," Ruhr Economic Papers 0201, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  8. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
  9. repec:ipg:wpaper:24 is not listed on IDEAS
  10. Roland Döhrn & Tobias Kitlinski & Torsten Schmidt & Simeon Vosen, 2010. "Die wirtschaftliche Entwicklung im Ausland zur Jahreswende 2009/2010 - Belasteter Aufschwung," RWI Konjunkturbericht, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 31, 03.
  11. Roland Döhrn & Tobias Kitlinski & Martin Micheli & Torsten Schmidt & Simeon Vosen & György Barabas & Heinz Gebhardt & Lina Zimmermann, 2010. "Die wirtschaftliche Entwicklung im Inland zur Jahresmitte 2010 - Aufschwung verliert an Fahrt," RWI Konjunkturbericht, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 46, 09.
  12. Gao, Lei & Mei, Bin, 2013. "Investor attention and abnormal performance of timberland investments in the United States," Forest Policy and Economics, Elsevier, vol. 28(C), pages 60-65.
  13. Mohamed Arouri & Amal Aouadi & Philippe Foulquier & Frédéric Teulon, 2013. "Can Information Demand Help to Predict Stock Market Liquidity ? Google it !," Working Papers 2013-024, Department of Research, Ipag Business School.

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