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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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File URL: http://repec.rwi-essen.de/files/REP_09_155.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 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. 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.
  2. 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.
  3. Nikos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Discussion Papers of DIW Berlin 899, DIW Berlin, German Institute for Economic Research.
  4. James A Wilcox, 2007. "Forecasting Components of Consumption with Components of Consumer Sentiment," Business Economics, Palgrave Macmillan, vol. 42(4), pages 22-32, October.
  5. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
  6. 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.
  7. 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.
  8. Croushore, Dean, 2004. "Do Consumer Confidence Indexes Help Forecast Consumer Spending in Real Time?," Discussion Paper Series 1: Economic Studies 2004,27, Deutsche Bundesbank, Research Centre.
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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 Doehrn & Christoph M. Schmidt, 2011. "Information or Institution? On the Determinants of Forecast Accuracy," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 9-27, February.
  3. 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.
  4. 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.
  5. repec:ipg:wpaper:24 is not listed on IDEAS
  6. 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.
  7. Yan Carrière-Swallow & Felipe Labbé, 2010. "Nowcasting With Google Trends in an Emerging Market," Working Papers Central Bank of Chile 588, Central Bank of Chile.
  8. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
  9. 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.
  10. 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.
  11. Roland Döhrn, 2011. "Konjunkturprognosen in bewegten Zeiten: Die Kunst des Unmöglichen?," RWI Materialien, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 26, 01.
  12. 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.
  13. 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.

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