"Google it!" Forecasting the US unemployment rate with a Google job search index
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.
|Date of creation:||30 Oct 2009|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://mpra.ub.uni-muenchen.de
More information through EDIRC
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.:
- 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.
- Busetti, Fabio & Marcucci, Juri, 2013. "Comparing forecast accuracy: A Monte Carlo investigation," International Journal of Forecasting, Elsevier, vol. 29(1), pages 13-27.
- West, Kenneth D, 1996.
"Asymptotic Inference about Predictive Ability,"
Econometric Society, vol. 64(5), pages 1067-84, September.
- Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March.
- Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
- 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.
- Sichel, Daniel E, 1993.
"Business Cycle Asymmetry: A Deeper Look,"
Western Economic Association International, vol. 31(2), pages 224-36, April.
- Sichel, D.E., 1988. "Business Cycle Asymmetry: A Deeper Look," Papers 85, Princeton, Department of Economics - Financial Research Center.
- 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.).
- 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).
- Graham Elliott & Thomas J. Rothenberg & James H. Stock, 1992.
"Efficient Tests for an Autoregressive Unit Root,"
NBER Technical Working Papers
0130, National Bureau of Economic Research, Inc.
- 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.
- 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.
- Kirstin Hubrich & Kenneth D. West, 2010.
"Forecast evaluation of small nested model sets,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
- Kirstin Hubrich & Kenneth D. West, 2008. "Forecast Evaluation of Small Nested Model Sets," NBER Working Papers 14601, National Bureau of Economic Research, Inc.
- Hubrich, Kirstin & West, Kenneth D., 2009. "Forecast evaluation of small nested model sets," Working Paper Series 1030, European Central Bank.
- 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.
- Philip Rothman, .
"Forecasting Asymmetric Unemployment Rates,"
9618, East Carolina University, Department of Economics.
- West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
- J. Bradford De Long & Lawrence H. Summers, 1986. "Are Business Cycles Symmetric?," NBER Working Papers 1444, National Bureau of Economic Research, Inc.
- 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.
- Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
- Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
- McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, vol. 31(3), pages 341-362, June.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:18248. 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: (Ekkehart Schlicht)
If references are entirely missing, you can add them using this form.