IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp4201.html
   My bibliography  Save this paper

Google Econometrics and Unemployment Forecasting

Author

Listed:
  • Askitas, Nikos

    (IZA)

  • Zimmermann, Klaus F.

    (University of Bonn)

Abstract

The current economic crisis requires fast information to predict economic behavior early, which is difficult at times of structural changes. This paper suggests an innovative new method of using data on internet activity for that purpose. It demonstrates strong correlations between keyword searches and unemployment rates using monthly German data and exhibits a strong potential for the method used.

Suggested Citation

  • Askitas, Nikos & Zimmermann, Klaus F., 2009. "Google Econometrics and Unemployment Forecasting," IZA Discussion Papers 4201, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp4201
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp4201.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    2. Amelie Constant & Klaus F. Zimmermann, 2008. "Im Angesicht der Krise: US-Präsidentschaftswahlen in transnationaler Sicht," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 75(44), pages 688-701.
    3. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikos Askitas & Klaus F. Zimmermann, 2009. "Prognosen aus dem Internet: weitere Erholung am Arbeitsmarkt erwartet," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 76(25), pages 402-408.
    2. Cedric Mbanga & Ali F. Darrat & Jung Chul Park, 2019. "Investor sentiment and aggregate stock returns: the role of investor attention," Review of Quantitative Finance and Accounting, Springer, vol. 53(2), pages 397-428, August.
    3. Heather R. Tierney & Bing Pan, 2012. "A poisson regression examination of the relationship between website traffic and search engine queries," Netnomics, Springer, vol. 13(3), pages 155-189, October.
    4. 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.
    5. Jun, Seung-Pyo & Park, Do-Hyung, 2016. "Consumer information search behavior and purchasing decisions: Empirical evidence from Korea," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 97-111.
    6. Jun, Seung-Pyo & Yoo, Hyoung Sun & Lee, Jae-Seong, 2021. "The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    7. Fabo, B., 2017. "Towards an understanding of job matching using web data," Other publications TiSEM b8b877f2-ae6a-495f-b6cc-9, Tilburg University, School of Economics and Management.
    8. Karolien Lenaerts & Miroslav Beblavý & Brian Fabo, 2016. "Prospects for utilisation of non-vacancy Internet data in labour market analysis—an overview," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-18, December.
    9. Véronique Flambard & Nicolas Vaillant & François-Charles Wolff, 2011. "Dating as Leisure," Chapters, in: Samuel Cameron (ed.), Handbook on the Economics of Leisure, chapter 9, Edward Elgar Publishing.
    10. Dean Fantazzini, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-27, November.
    11. Askitas, Nikos & Zimmermann, Klaus F., 2009. "Googlemetrie und Arbeitsmarkt in der Wirtschaftskrise," IZA Standpunkte 17, Institute of Labor Economics (IZA).
    12. Omid Zamani & Thomas Bittmann & Jens‐Peter Loy, 2024. "Does the internet bring food prices closer together? Exploring search engine query data in Iran," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(2), pages 688-715, June.
    13. Bilal Mehmood & Syed Hassan Raza & Mahwish Rana & Huma Sohaib & Muhammad Azhar Khan, 2014. "Triangular Relationship between Energy Consumption, Price Index and National Income in Asian Countries: A Pooled Mean Group Approach in Presence of Structural Breaks," International Journal of Energy Economics and Policy, Econjournals, vol. 4(4), pages 610-620.
    14. Law, Siong Hook & Tan, Hui & baharumshah, ahmad, 1999. "Financial Liberalization in ASEAN and the Fisher Hypothesis," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 33, pages 65-86.
    15. Shahbaz, Muhammad & Hoang, Thi Hong Van & Mahalik, Mantu Kumar & Roubaud, David, 2017. "Energy consumption, financial development and economic growth in India: New evidence from a nonlinear and asymmetric analysis," Energy Economics, Elsevier, vol. 63(C), pages 199-212.
    16. Levent, Korap, 2007. "Modeling purchasing power parity using co-integration: evidence from Turkey," MPRA Paper 19584, University Library of Munich, Germany.
    17. Ranjan Aneja & Umer J. Banday & Tanzeem Hasnat & Mustafa Koçoglu, 2017. "Renewable and Non-renewable Energy Consumption and Economic Growth: Empirical Evidence from Panel Error Correction Model," Jindal Journal of Business Research, , vol. 6(1), pages 76-85, June.
    18. Yih-Ing Hser & Haikang Shen & Chih-Ping Chou & Stephen C. Messer & M. Douglas Anglin, 2001. "Analytic Approaches for Assessing Long-Term Treatment Effects," Evaluation Review, , vol. 25(2), pages 233-262, April.
    19. Zamani, Mehrzad, 2007. "Energy consumption and economic activities in Iran," Energy Economics, Elsevier, vol. 29(6), pages 1135-1140, November.
    20. Muhammad Zia Ullah Khan & Muhammad Illyas & Muqqadas Rahman & Chaudhary Abdul Rahman, 2015. "Money Monetization and Economic Growth in Pakistan," International Journal of Economics and Empirical Research (IJEER), The Economics and Social Development Organization (TESDO), vol. 3(4), pages 184-192, April.

    More about this item

    Keywords

    unemployment; time-series analysis; internet; Google; keyword search; search engine; predictions;
    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
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Lists

    This item is featured on the following reading lists, Wikipedia, or ReplicationWiki pages:
    1. Economic Logic blog

    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:iza:izadps:dp4201. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.