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Can Google data help predict French youth unemployment?

Author

Listed:
  • Y. Fondeur

    (CEE - Centre d'études de l'emploi - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche - Ministère du Travail, de l'Emploi et de la Santé)

  • F. Karamé

    (GAINS - Groupe d'Analyse des Itinéraires et des Niveaux Salariaux - UM - Le Mans Université, TEPP - Travail, Emploi et Politiques Publiques - UPEM - Université Paris-Est Marne-la-Vallée - CNRS - Centre National de la Recherche Scientifique, CEE - Centre d'études de l'emploi - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche - Ministère du Travail, de l'Emploi et de la Santé, CEPREMAP - Centre pour la recherche économique et ses applications - ECO ENS-PSL - Département d'économie de l'ENS-PSL - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres)

Abstract

According to the growing "Google econometrics" literature, Google queries may help predict economic activity. The aim of our paper is to test whether these data can enhance predictions of youth unemployment in France. Because we have weekly series on web search queries and monthly series on unemployment for 15- to 24-year olds, we use the unobserved components approach in order to exploit all available information. Our model is estimated with a modified version of the Kalman filter, taking into account the twofold issue of non-stationarity and multiple frequencies in our data. We find that including Google data improves unemployment predictions relative to a competing model that does not employ search data queries.

Suggested Citation

  • Y. Fondeur & F. Karamé, 2013. "Can Google data help predict French youth unemployment?," Post-Print hal-02297071, HAL.
    • Handle: RePEc:hal:journl:hal-02297071
    DOI: 10.1016/j.econmod.2012.07.017
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    More about this item

    Keywords

    Google econometrics; Forecasting; Nowcasting; Unemployment; Unobserved components; Diffuse initialization; Kalman filter; Univariate treatment of time series; Smoothing; Multivariate models;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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