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“Google it!”Forecasting the US Unemployment Rate with a Google Job Search index

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  • Francesco D’Amuri

    (Economic Research Department)

  • Juri Marcucci

    (Bank of Italy)

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 Fondazione Eni Enrico Mattei in its series Working Papers with number 2010.31.

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Date of creation: Mar 2010
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Handle: RePEc:fem:femwpa:2010.31

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Keywords: Google Econometrics; Forecast Comparison; Keyword search; US Unemployment; Time Series Models;

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References

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  1. Sichel, Daniel E, 1993. "Business Cycle Asymmetry: A Deeper Look," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 224-36, April.
  2. Philip Rothman, 1998. "Forecasting Asymmetric Unemployment Rates," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 164-168, February.
  3. Hubrich, Kirstin & West, Kenneth D., 2009. "Forecast evaluation of small nested model sets," Working Paper Series 1030, European Central Bank.
  4. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  5. McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, vol. 31(3), pages 341-362, June.
  6. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
  7. Golan, Amos & Perloff, Jeffrey M, 2002. "Superior forecasts of the U.S. unemployment rate using a nonparametric method," CUDARE Working Paper Series 956, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  8. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
  9. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-36, July.
  10. 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.
  11. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  12. Kenneth D. West, 1994. "Asymptotic Inference About Predictive Ability," Macroeconomics 9410002, EconWPA.
  13. Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March.
  14. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  15. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-28, April.
  16. J. Bradford De Long & Lawrence H. Summers, 1986. "Are Business Cycles Symmetric?," NBER Working Papers 1444, National Bureau of Economic Research, Inc.
  17. Busetti, Fabio & Marcucci, Juri, 2013. "Comparing forecast accuracy: A Monte Carlo investigation," International Journal of Forecasting, Elsevier, vol. 29(1), pages 13-27.
  18. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
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Cited by:
  1. Konstantin A. Kholodilin & Boriss Siliverstovs, 2010. "Measuring Regional Inequality by Internet Car Price Advertisements: Evidence for Germany," Discussion Papers of DIW Berlin 1036, DIW Berlin, German Institute for Economic Research.
  2. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
  3. Torsten Schmidt & Simeon Vosen, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 0382, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  4. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.
  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," Working Papers 2013-04, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
  6. David Iselin & Boriss Siliverstovs, 2013. "Mit Zeitungen Konjunkturprognosen erstellen: Eine Vergleichsstudie für die Schweiz und Deutschland," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 7(3), pages 104-117, September.
  7. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
  8. Maria De Paola & Vincenzo Scoppa, 2013. "Consumers’ Reactions to Negative Information on Product Quality: Evidence from Scanner Data," Review of Industrial Organization, Springer, vol. 42(3), pages 235-280, May.
  9. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
  10. 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.
  11. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.

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