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Can Google Data Help Predict French Youth Unemployment?

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  • Frédéric Karamé

    ()
    (EPEE (Université d’Evry-Val-d’Essonne), TEPP (FR CNRS n°3126), DYNARE Team (Cepremap) and Centre d’Etudes de l‘Emploi)

  • Yannick Fondeur

    ()
    (Centre d’Etudes de l’Emploi)

Abstract

According to the rising “Google econometrics” literature, Google queries may help predict economic activity. The aim of our paper is to test if these data can enhance predictions for youth unemployment in France. As we have on the one hand weekly series on web search queries and on the other hand monthly series on unemployment for the 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 issues of non-stationarity and multiple frequencies in our data. We find that including Google data improves unemployment predictions relatively to a competing model without search data queries.

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Bibliographic Info

Paper provided by Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne in its series Documents de recherche with number 12-03.

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Length: 22 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:eve:wpaper:12-03

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Keywords: Google econometrics; forecasting; nowcasting; unemployment; unobserved components; diffuse initialization; Kalman filter; univariate treatment of time series; smoothing; multivariate models;

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Cited by:
  1. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
  2. 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.

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