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Google Searches as a Means of Improving the Nowcasts of Key Macroeconomic Variables

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  • Konstantin A. Kholodilin
  • Maximilian Podstawski
  • Boriss Siliverstovs
  • Constantin Bürgi

Abstract

The Google Insights data are a collection of recorded Internet searches for a huge number of the keywords, which are available since January 2004. These searches represent a kind of revealed perceptions of Internet users, which are a (possibly not entirely representative) sample of the general public. These data can be used to improve the short-term forecasts or nowcasts of various macroeconomic variables. In this paper, we compare the nowcasts of the growth rates of the real US private consumption based on both the conventional consumer confidence indicators and the Google indicators. The latter are extracted from the Google searches using the principal component analysis. It is shown that the Google indicators are especially successful at predicting private consumption in times of economic trouble, for they are 20% more accurate than the best alternative during the 2008m1-2009m5 forecast period. In addition, Google indicators are available at weekly frequency and not subject to revisions. This makes them an excellent source of information for the macroeconomic forecasting.

Suggested Citation

  • Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs & Constantin Bürgi, 2009. "Google Searches as a Means of Improving the Nowcasts of Key Macroeconomic Variables," Discussion Papers of DIW Berlin 946, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp946
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    Cited by:

    1. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    2. repec:eee:touman:v:46:y:2015:i:c:p:386-397 is not listed on IDEAS
    3. Azusa Matsumoto & Kohei Matsumura & Noriyuki Shiraki, 2013. "Potential of Search Data in Assessment of Current Economic Conditions," Bank of Japan Research Papers 2013-04-18, Bank of Japan.

    More about this item

    Keywords

    Google indicators; forecasting; 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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