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Forecasting private consumption: survey‐based indicators vs. Google trends

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  • Simeon Vosen
  • Torsten Schmidt

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 offer significant benefits to forecasters of private consumption. Copyright (C) 2011 John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 30 (2011)
Issue (Month): 6 (September)
Pages: 565-578

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Handle: RePEc:jof:jforec:v:30:y:2011:i:6:p:565-578

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Keywords: Google Trends ; private consumption ; forecasting ; consumer sentiment indicators ;

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  1. James A Wilcox, 2007. "Forecasting Components of Consumption with Components of Consumer Sentiment," Business Economics, Palgrave Macmillan, vol. 42(4), pages 22-32, October.
  2. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
  3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(1), pages 134-44, January.
  4. Nikos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Research Notes of the German Council for Social and Economic Data 41, German Council for Social and Economic Data (RatSWD).
  5. 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.
  6. 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.
  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, Federal Reserve Bank of New York, issue Jun, pages 59-78.
  8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
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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, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne 12-03, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
  2. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
  3. 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.
  4. Torsten Schmidt & Simeon Vosen, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen 0382, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  5. Roland Döhrn, 2011. "Konjunkturprognosen in bewegten Zeiten: Die Kunst des Unmöglichen?," RWI Materialien, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 26, 01.
  6. repec:ipg:wpaper:24 is not listed on IDEAS
  7. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, Elsevier, vol. 9(2), pages 103-110.
  8. Roland Döhrn & Christoph M. Schmidt, 2010. "Information or Institution? – On the Determinants of Forecast Accuracy," Ruhr Economic Papers, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen 0201, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  9. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
  10. Yan Carrière-Swallow & Felipe Labbé, 2010. "Nowcasting With Google Trends in an Emerging Market," Working Papers Central Bank of Chile, Central Bank of Chile 588, Central Bank of Chile.
  11. 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.
  12. 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.
  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.
  14. 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.

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