Can Google data help predict French youth unemployment?

Y. Fondeur and Frédéric Karamé
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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

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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.

Keywords: Google econometrics; Forecasting; Nowcasting; Unemployment; Unobserved components; Diffuse initialization; Kalman filter; Univariate treatment of time series; Smoothing; Multivariate models (search for similar items in EconPapers)
Date: 2013-01
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Citations: View citations in EconPapers (45)

Published in Economic Modelling, 2013, 30, pp.117-125. ⟨10.1016/j.econmod.2012.07.017⟩

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Journal Article: Can Google data help predict French youth unemployment? (2013)
Working Paper: Can Google Data Help Predict French Youth Unemployment? (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02297071

DOI: 10.1016/j.econmod.2012.07.017

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