IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp946.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.343057.de/dp946.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    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;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:diw:diwwpp:dp946. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/diwbede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.