Advanced Search
MyIDEAS: Login

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

Contents:

Author Info

  • 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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.feem.it/userfiles/attach/20103261238204NDL2010-031.pdf
Download Restriction: no

Bibliographic Info

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

as in new window
Length:
Date of creation: Mar 2010
Date of revision:
Handle: RePEc:fem:femwpa:2010.31

Contact details of provider:
Postal: Corso Magenta, 63 - 20123 Milan
Phone: 0039-2-52036934
Fax: 0039-2-52036946
Email:
Web page: http://www.feem.it/
More information through EDIRC

Related research

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

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  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.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Maria De Paola & Vincenzo Scoppa, 2010. "Consumers’ Reactions To Negative Information On Product Quality: Evidence From Scanner Data," Working Papers 201012, Università della Calabria, Dipartimento di Scienze Economiche, Statistiche e Finanziarie (Ex Dipartimento di Economia e Statistica).
  2. 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.
  3. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
  4. 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.
  5. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
  6. 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.
  7. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
  8. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Health and Well-Being in the Crisis," IZA Discussion Papers 5601, Institute for the Study of Labor (IZA).
  9. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:fem:femwpa:2010.31

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (barbara racah).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.