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Can Google Data Help Predict French Youth Unemployment?

  • 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)

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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File URL: http://epee.univ-evry.fr/RePEc/2012/12-03.pdf
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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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  1. Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
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