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Do Google Searches Help in Nowcasting Private Consumption?: A Real-Time Evidence for the US

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  • Konstantin A. Kholodilin
  • Maximilian Podstawski
  • Boriss Siliverstovs

Abstract

In this paper, we investigate whether the Google search activity can help in nowcasting the year-on-year growth rates of monthly US private consumption using a real-time data set. The Google-based forecasts are compared to those based on a benchmark AR(1) model and the models including the consumer surveys and financial indicators. According to the Diebold-Mariano test of equal predictive ability, the null hypothesis can be rejected suggesting that Google-based forecasts are significantly more accurate than those of the benchmark model. At the same time, the corresponding null hypothesis cannot be rejected for models with consumer surveys and financial variables. Moreover, when we apply the test of superior predictive ability (Hansen, 2005) that controls for possible data-snooping biases, we are able to reject the null hypothesis that the benchmark model is not inferior to any alternative model forecasts. Furthermore, the results of the model confidence set (MCS) procedure (Hansen et al., 2005) suggest that the autoregressive benchmark is not selected into a set of the best forecasting models. Apart from several Google-based models, the MCS contains also some models including survey-based indicators and financial variables. We conclude that Google searches do help improving the nowcasts of the private consumption in US.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.356220.de/dp997.pdf
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Bibliographic Info

Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 997.

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Length: 27 p.
Date of creation: 2010
Date of revision:
Handle: RePEc:diw:diwwpp:dp997

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Keywords: Google indicators; real-time nowcasting; principal components; US private consumption;

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  1. Nowcasting consumption with Google
    by Adam Ozimek in Modeled Behavior on 2010-06-07 11:26:11
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Cited by:
  1. Rose, Andrew K & Spiegel, Mark, 2011. "Dollar Illiquidity and Central Bank Swap Arrangements During the Global Financial Crisis," CEPR Discussion Papers 8557, C.E.P.R. Discussion Papers.
  2. Kholodilin, Konstantin A. & Siliverstovs, Boriss, 2012. "Measuring regional inequality by internet car price advertisements: Evidence for Germany," Economics Letters, Elsevier, vol. 116(3), pages 414-417.
  3. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle: A comparative study for Germany and Switzerland," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
  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. David Iselin & Boriss Siliverstovs, 2013. "Mit Zeitungen Konjunkturprognosen erstellen: Eine Vergleichsstudie für die Schweiz und Deutschland," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 7(3), pages 104-117, September.
  6. 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.
  7. Konstantin A. Kholodilin & Tobias Thomas & Dirk Ulbricht, 2014. "Do Media Data Help to Predict German Industrial Production?," Discussion Papers of DIW Berlin 1393, DIW Berlin, German Institute for Economic Research.

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