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Can Google data help predict French youth unemployment?

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  • Fondeur, Y.
  • Karamé, F.

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.

Suggested Citation

  • Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
  • Handle: RePEc:eee:ecmode:v:30:y:2013:i:c:p:117-125
    DOI: 10.1016/j.econmod.2012.07.017
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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Kuttner, Kenneth N, 1994. "Estimating Potential Output as a Latent Variable," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 361-368, July.
    3. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    4. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Koopman, Siem Jan & Franses, Philip Hans, 2002. " Constructing Seasonally Adjusted Data with Time-Varying Confidence Intervals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(5), pages 509-526, December.
    11. repec:adr:anecst:y:1999:i:54 is not listed on IDEAS
    12. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    13. Konstantin Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?," KOF Working papers 10-256, KOF Swiss Economic Institute, ETH Zurich.
    14. 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.
    15. Jennifer L. Castle & Nicholas W.P. Fawcett & David F. Hendry, 2009. "Nowcasting Is Not Just Contemporaneous Forecasting," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210(1), pages 71-89, October.
    16. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    17. Peter K. Clark, 1987. "The Cyclical Component of U. S. Economic Activity," The Quarterly Journal of Economics, Oxford University Press, vol. 102(4), pages 797-814.
    18. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    19. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    20. Clark, Peter K., 1989. "Trend reversion in real output and unemployment," Journal of Econometrics, Elsevier, vol. 40(1), pages 15-32, January.
    21. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    22. 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, January.
    23. Laurent Ferrara & Dominique Guégan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 186-199.
    24. 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.
    25. repec:hal:journl:halshs-00460461 is not listed on IDEAS
    26. repec:adr:anecst:y:1999:i:54:p:05 is not listed on IDEAS
    27. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    28. repec:zbw:rwirep:0155 is not listed on IDEAS
    29. 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.
    30. 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, January.
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    Cited by:

    1. repec:eee:jeborg:v:145:y:2018:i:c:p:1-23 is not listed on IDEAS
    2. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
    3. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 2-12, April.
    4. Ronald MacDonald & Xuxin Mao, 2015. "An Alternative way of Predicting the Outcome of the Scottish Independence Referendum: The Information in the Ether," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-69, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. repec:eee:intfor:v:33:y:2017:i:4:p:801-816 is not listed on IDEAS
    6. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    7. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    8. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    9. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2018. "Volatility persistence and asymmetry under the microscope: The role of information demand for gold and oil," Working Paper series 18-13, Rimini Centre for Economic Analysis.
    10. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    11. Ronald MacDonald & Xuxin Mao, "undated". "An Alternative way of predicting the putcome of the Scottish Independence Referendum: the information in the Ether," Working Papers 2015_05, Business School - Economics, University of Glasgow.
    12. Branislav Saxa, 2014. "Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand," Working Papers 2014/14, Czech National Bank, Research Department.
    13. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    14. repec:eee:joepsy:v:63:y:2017:i:c:p:1-16 is not listed on IDEAS
    15. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
    16. Voraprapa Nakavachara & Nuarpear Warn Lekfuangfu, 2017. "Predicting the Present Revisited: The Case of Thailand," PIER Discussion Papers 70, Puey Ungphakorn Institute for Economic Research, revised Oct 2017.
    17. 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.
    18. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.

    More about this item

    Keywords

    Google econometrics; Forecasting; Nowcasting; Unemployment; Unobserved components; Diffuse initialization; Kalman filter; Univariate treatment of time series; Smoothing; Multivariate models;

    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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