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Can Google data help predict French youth unemployment?

  • Fondeur, Y.
  • Karamé, F.

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.

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File URL: http://www.sciencedirect.com/science/article/pii/S0264999312002490
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Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 30 (2013)
Issue (Month): C ()
Pages: 117-125

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Handle: RePEc:eee:ecmode:v:30:y:2013:i:c:p:117-125
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/30411

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  1. Ferrara, L. & Koopman, S J., 2010. "Common business and housing market cycles in the Euro area from a multivariate decomposition," Working papers 275, Banque de France.
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  9. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00460461, HAL.
  10. Schmidt, Torsten & Vosen, Simeon, 2009. "Forecasting Private Consumption: Survey-based Indicators vs. Google Trends," Ruhr Economic Papers 155, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  11. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
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  13. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?: A Real-Time Evidence for the US," Discussion Papers of DIW Berlin 997, DIW Berlin, German Institute for Economic Research.
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  16. Matthieu Cornec, 2006. "Analyse factorielle dynamique multifréquence appliquée à la datation de la conjoncture française," Economie & Prévision, La Documentation Française, vol. 172(1), pages 29-43.
  17. D'Amuri, Francesco & Marcucci, Juri, 2009. "'Google it!' Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
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  20. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
  21. Catherine Doz & Fabrice Lenglart, 1999. "Analyse factorielle dynamique : test du nombre de facteurs, estimation et application à l'enquête de conjoncture dans l'industrie," Annals of Economics and Statistics, GENES, issue 54, pages 91-127.
  22. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, December.
  23. Kuttner, Kenneth N, 1994. "Estimating Potential Output as a Latent Variable," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 361-68, July.
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  25. Matthieu Cornec & Thierry Deperraz, 2006. "Un nouvel indicateur synthétique mensuel résumant le climat des affaires dans les services en France," Économie et Statistique, Programme National Persée, vol. 395(1), pages 13-38.
  26. Matthieu Cornec, 2006. "Analyse factorielle dynamique multifréquence appliquée à la datation de la conjoncture française," Économie et Prévision, Programme National Persée, vol. 172(1), pages 29-43.
  27. S. J. Koopman & J. Durbin, 2003. "Filtering and smoothing of state vector for diffuse state-space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 85-98, 01.
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