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'Google it!' Forecasting the US unemployment rate with a Google job search index

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  • D'Amuri, Francesco
  • Marcucci, Juri

Abstract

We suggest the use of an Internet job-search indicator (the Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt both our preferred leading indicator (GI), the more standard initial claims or combinations of both. We find that models augmented with the GI outperform the traditional ones in predicting the monthly unemployment rate, even in most state-level forecasts and in comparison with the Survey of Professional Forecasters.

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Bibliographic Info

Paper provided by Institute for Social and Economic Research in its series ISER Working Paper Series with number 2009-32.

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Date of creation: 18 Nov 2009
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Publication status: published
Handle: RePEc:ese:iserwp:2009-32

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Postal: Publications Office, Institute for Social and Economic Research, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ UK
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  1. Fabio Busetti & Juri Marcucci & Giovanni Veronese, 2009. "Comparing forecast accuracy: A Monte Carlo investigation," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area 723, Bank of Italy, Economic Research and International Relations Area.
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  8. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
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  12. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
  13. Amos Golan & Jeffrey M. Perloff, 2004. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 433-438, February.
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Cited by:
  1. Boriss Siliverstovs & Konstantin Kholodilin, 2012. "Measuring Regional Inequality by Internet Car Price Advertisements: Evidence for Germany," ERSA conference papers ersa12p911, European Regional Science Association.
  2. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
  3. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
  4. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, Elsevier, vol. 30(C), pages 117-125.
  5. Michael R. Baye & Babur De los Santos & Matthijs R. Wildenbeest, 2013. "Searching for Physical and Digital Media: The Evolution of Platforms for Finding Books," NBER Working Papers 19519, National Bureau of Economic Research, Inc.
  6. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
  7. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Health and Well-Being in the Crisis," IZA Discussion Papers 5601, Institute for the Study of Labor (IZA).
  8. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle: A comparative study for Germany and Switzerland," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
  9. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
  10. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers, Stanford Institute for Economic Policy Research 10-020, Stanford Institute for Economic Policy Research.
  11. Maria De Paola & Vincenzo Scoppa, 2013. "Consumers’ Reactions to Negative Information on Product Quality: Evidence from Scanner Data," Review of Industrial Organization, Springer, Springer, vol. 42(3), pages 235-280, May.
  12. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption? A Real-Time Evidence for the US," KOF Working papers 10-256, KOF Swiss Economic Institute, ETH Zurich.
  13. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, Elsevier, vol. 9(2), pages 103-110.
  14. Torsten Schmidt & Simeon Vosen, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen 0382, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  15. David Iselin & Boriss Siliverstovs, 2013. "Mit Zeitungen Konjunkturprognosen erstellen: Eine Vergleichsstudie für die Schweiz und Deutschland," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, KOF Swiss Economic Institute, ETH Zurich, vol. 7(3), pages 104-117, September.

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