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Nowcasting With Google Trends in an Emerging Market

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  • Yan Carrière-Swallow
  • Felipe Labbé

Abstract

Most economic variables are released with a lag, making it difficult for policy-makers to make an accurate assessment of current conditions. This paper explores whether observing Internet browsing habits can inform practitioners about real-time aggregate consumer behavior in an emerging market. Using data on Google search queries, we introduce a simple index of interest in automobile purchases in Chile and test whether it improves the fit and efficiency of nowcasting models for automobile sales. We also examine to what extent our index helps us identify turning points in sales data. Despite relatively low rates of Internet usage among the population, we find that models incorporating our Google Trends Automotive Index outperform benchmark specifications in both in-sample and outof- sample nowcasts while providing substantial gains in information delivery times.

Suggested Citation

  • 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.
  • Handle: RePEc:chb:bcchwp:588
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    References listed on IDEAS

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    1. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
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    5. Shiu-Sheng Chen, 2005. "A note on in-sample and out-of-sample tests for Granger causality," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 453-464.
    6. 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.
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. Della Penna, Nicolas & Huang, Haifang, 2009. "Constructing Consumer Sentiment Index for U.S. Using Google Searches," Working Papers 2009-26, University of Alberta, Department of Economics, revised 01 Feb 2010.
    9. Trefler, Daniel, 1995. "The Case of the Missing Trade and Other Mysteries," American Economic Review, American Economic Association, vol. 85(5), pages 1029-1046, December.
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    Cited by:

    1. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 2-12, April.
    2. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    3. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters,in: Economic Analysis of the Digital Economy, pages 119-135 National Bureau of Economic Research, Inc.
    4. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
    5. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
    6. Olivier Gergaud & Victor Ginsburgh, 2016. "Evaluating the Economic Effects of Cultural Events," Working Papers ECARES ECARES 2016-24, ULB -- Universite Libre de Bruxelles.
    7. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    8. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    9. Gantiah Wuryandani & Indri Mardiani, 2015. "Surveys as leading information to support central bank policy formulation: the case of Indonesia," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39 Bank for International Settlements.

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