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

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  • D'Amuri, Francesco/FD
  • Marcucci, Juri/JM

Abstract

In this paper we suggest the use of an internet job-search indicator (Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample comparison of many forecasting models. With respect to the previous literature we concentrate on the monthly series extending the out-of-sample forecast comparison with models that adopt both our preferred leading indicator (GI), the more standard initial claims or combinations of both. Our results show that the GI indeed helps in predicting the US unemployment rate even after controlling for the effects of data snooping. Robustness checks show that models augmented with the GI perform better than traditional ones even in most state-level forecasts and in comparison with the Survey of Professional Forecasters' federal level predictions.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 18248.

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Date of creation: 30 Oct 2009
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Handle: RePEc:pra:mprapa:18248

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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. Nikos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Discussion Papers of DIW Berlin 899, DIW Berlin, German Institute for Economic Research.
  2. Kirstin Hubrich & Kenneth D. West, 2009. "Forecast evaluation of small nested model sets," Working Paper Series 1030, European Central Bank.
  3. Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March.
  4. Sichel, Daniel E, 1993. "Business Cycle Asymmetry: A Deeper Look," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 224-36, April.
  5. 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.
  6. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September.
  7. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  8. Golan, Amos & Perloff, Jeffrey M., 2002. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2bw559zk, Department of Agricultural & Resource Economics, UC Berkeley.
  9. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
  10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  11. McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, vol. 31(3), pages 341-362, June.
  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. 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.
  14. 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.
  15. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  16. Fabio Busetti & Juri Marcucci & Giovanni Veronese, 2009. "Comparing forecast accuracy: A Monte Carlo investigation," Temi di discussione (Economic working papers) 723, Bank of Italy, Economic Research and International Relations Area.
  17. Philip Rothman, . "Forecasting Asymmetric Unemployment Rates," Working Papers 9618, East Carolina University, Department of Economics.
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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.
  6. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
  7. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
  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. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Health and Well-Being in the Crisis," IZA Discussion Papers 5601, Institute for the Study of Labor (IZA).

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