The predictive power of Google searches in forecasting unemployment
We suggest the use of an index of Internet job-search intensity (the Google Index, GI) as the best leading indicator to predict the US monthly unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt 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 unemployment rate for different out-of-sample intervals that start before, during and after the Great Recession. Google-based models also outperform standard ones in most state-level forecasts and in comparison with the Survey of Professional Forecasters. These results survive a falsification test and are also confirmed when employing different keywords. Based on our results for the unemployment rate, we believe that there will be an increasing number of applications using Google query data in other fields of economics.
|Date of creation:||Nov 2012|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.bancaditalia.it
More information through EDIRC
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.:
- 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.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- West, K.D., 1994.
"Asymptotic Inference About Predictive Ability,"
9417, Wisconsin Madison - Social Systems.
- Daniel E. Sichel, 1989.
"Business cycle asymmetry: a deeper look,"
Working Paper Series / Economic Activity Section
93, Board of Governors of the Federal Reserve System (U.S.).
- Chi-Young Choi & Young-Kyu Moh, 2007. "How useful are tests for unit-root in distinguishing unit-root processes from stationary but non-linear processes?," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 82-112, 03.
- Regis Barnichon & Christopher J. Nekarda, 2013.
"The ins and outs of forecasting unemployment: Using labor force flows to forecast the labor market,"
Finance and Economics Discussion Series
2013-19, Board of Governors of the Federal Reserve System (U.S.).
- Regis Barnichon & Christopher J. Nekarda, 2012. "The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 45(2 (Fall)), pages 83-131.
- Paolo Pinotti, 2012.
"The Economic Costs of Organized Crime: Evidence from Southern Italy,"
054, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
- Paolo Pinotti, 2012. "The economic costs of organized crime: evidence from southern Italy," Temi di discussione (Economic working papers) 868, Bank of Italy, Economic Research and International Relations Area.
- Gary Koop & Simon M. Potter, 2004.
"Dynamic asymmetries in US unemployment,"
ESE Discussion Papers
15, Edinburgh School of Economics, University of Edinburgh.
- McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, vol. 31(3), pages 341-362, June.
- Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
- Benjamin Edelman, 2012. "Using Internet Data for Economic Research," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 189-206, Spring.
- 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.
- 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.
- 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.
- Nikos Askitas & Klaus F. Zimmermann, 2009.
"Google Econometrics and Unemployment Forecasting,"
Discussion Papers of DIW Berlin
899, DIW Berlin, German Institute for Economic Research.
- 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.
- Nikos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Research Notes of the German Council for Social and Economic Data 41, German Council for Social and Economic Data (RatSWD).
- Askitas, Nikos & Zimmermann, Klaus F., 2009. "Google Econometrics and Unemployment Forecasting," IZA Discussion Papers 4201, Institute for the Study of Labor (IZA).
- Felipe Aparicio & Alvaro Escribano & Ana E. Sipols, 2006. "Range Unit-Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and Outliers," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(4), pages 545-576, 07.
- Busetti, Fabio & Marcucci, Juri, 2013.
"Comparing forecast accuracy: A Monte Carlo investigation,"
International Journal of Forecasting,
Elsevier, vol. 29(1), pages 13-27.
- 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.
- Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.
When requesting a correction, please mention this item's handle: RePEc:bdi:wptemi:td_891_12. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.