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

Author

Listed:
  • 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.

Suggested Citation

  • Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?: A Real-Time Evidence for the US," Discussion Papers of DIW Berlin 997, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp997
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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.356220.de/dp997.pdf
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Nowcasting consumption with Google
      by Adam Ozimek in Modeled Behavior on 2010-06-07 16:26:11

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    3. repec:eee:jeborg:v:145:y:2018:i:c:p:1-23 is not listed on IDEAS
    4. 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.
    5. repec:wly:jforec:v:36:y:2017:i:5:p:483-496 is not listed on IDEAS
    6. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    7. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    8. Dirk Ulbricht & Konstantin A. Kholodilin & Tobias Thomas, 2017. "Do Media Data Help to Predict German Industrial Production?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 483-496, August.
    9. 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.
    10. repec:bis:bisifc:50-17 is not listed on IDEAS
    11. Ikhoon, Jang & Young Chan, Choe, 2016. "Forecasting Agri-food Consumption Using the Keyword Volume Index from Search Engine Data," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236124, Agricultural and Applied Economics Association.
    12. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2019. "Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 180-197, February.
    13. Rose, Andrew K. & Spiegel, Mark M., 2012. "Dollar illiquidity and central bank swap arrangements during the global financial crisis," Journal of International Economics, Elsevier, vol. 88(2), pages 326-340.
    14. 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.
    15. 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.
    16. María Gil & Javier J. Pérez & Alberto Urtasun, 2019. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," IFC Bulletins chapters,in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50 Bank for International Settlements.
    17. 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.
    18. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    19. Gomes, Pedro & Taamouti, Abderrahim, 2016. "In search of the determinants of European asset market comovements," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 103-117.

    More about this item

    Keywords

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

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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