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

D'Amuri, Francesco and Marcucci, Juri (2009): "Google it!" Forecasting the US unemployment rate with a Google job search index.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Google econometrics, Forecast comparison, Keyword search, US unemployment, Time series models.
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers > J60 - General; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E27 - Forecasting and Simulation: Models and Applications; J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers > J64 - Unemployment: Models, Duration, Incidence, and Job Search; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Askitas, Nikos and Zimmermann, Klaus F. 2009. “Google Econometrics and Unemployment Forecasting”, IZA Discussion Paper, (4201).

Busetti, Fabio, Marcucci, Juri and Veronese, Giovanni. 2009. “Comparing Forecast Accuracy: A Monte Carlo Investigation”, Bank of Italy, Discussion paper n. 723.

Choi, Hyunyoung and Varian, Hal. 2009. “Predicting Initial Claims for Unemployment Benefits”,Google technical report.

D’Amuri, Francesco. 2009. “Predicting unemployment in short samples with internet job search query data”, MPRA working paper n. 18403.

DeLong, James B. and Summers, Lawrence H. 1986. “Are Business Cycles Symmetrical?”, in The American Buiness Cycle, Continuity and Changes, ed. R. J. Gorton, Chicago: University of Chicago Press for NBER.

Diebold, Francis X. and Mariano, Roberto S. 1995. “Comparing Predictive Accuracy”, Journal of Business & Economic Statistics, 13: 253-263.

Elliott, Graham, Rothenberg, Thomas J., and Stock, James H. 1996. “Efficient Tests for an Autoregressive Unit Root,” Econometrica, 64: 813-836.

Ginsberg, Jeremy, Mohebbi, Matthew H., Patel, Rajan S., Brammer Lynnette, Smolinski, Mark S. and Brilliant Larry. 2009. “Detecting Influenza epidemics using Search Engine Query Data”, Nature, 457: 1012-1014.

Golan, Amos and Perloff, Jeffrey M. 2004. “Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method”, The Review of Economics and Statistics, February, 86(1): 433-438.

Hansen, Peter R. 2005. “A Test for Superior Predictive Ability”, Journal of Business and Economic Statistics, 23: 365-380.

Harvey, David I., Leybourne, Stephen J. and Newbold, Paul. 1998. “Tests for Forecast Encompassing”, Journal of Business & Economic Statistics, 16: 254-259.

Hastie, Trevor J., Tibshirani, Rob J. 1990. “Generalized Additive Models”, Chapman and Hall Ltd., London.

Hubrich, Kirstin, and West, Kenneth D. 2009. “Forecast Evaluation of Small Nested Model Sets”, ECB Working paper n. 1030.

Koop, Gary, and Potter, Simon M. 1999. “Dynamic Asymmetries in U.S. Unemployment”, Journal of Business and Economic Statistics, 17(3): 298-312.

McQueen, Grant, and Thorley, Steven. 1993. “Asymmetric Business Cycle Turning Points”, Journal of Monetary Economics, 31: 341-362.

Montgomery, Alan L., Zarnowitz, Victor, Tsay, Ruey S. and Tiao, George C. 1998. “Forecasting the U.S. Unemployment Rate”, Journal of the American Statistical Association, June, 93(442): 478-493.

Neftci, Salih N. 1984. “Are Economic Time Series Asymmetric Over the Business Cycles?”, Journal of Political Economy, 85: 281-291.

Politis, Dimitris N. and Romano, Joseph P. 1994. “The Stationary Bootstrap”, Journal of The American Statistical Association, 89(428): 1303-1313.

Proietti, Tommaso. 2003. “Forecasting the US Unemployment Rate”, Computational Statistics & Data Analysis, 42: 451-476.

Rothman, Philip. 1998. “Forecasting Asymmetric Unemployment Rates”, The Review of Economics and Statistics, February, 80(1): 164-168.

Sichel, Daniel E.. 1993. “Business Cycle Asymmetry: A Deeper Look”, Economic Enquiry, 31: 224-236.

Suhoy, Tanya. 2009. “Query Indices and a 2008 Downturn ”, Bank of Israel Discussion Paper (2009.06).

Wallis, Kenneth. 1987. “Time Series Analysis of Bounded Economic Variables”, Journal of Time Series Analysis, 8: 115-123.

West, Kenneth D. 1996. “Asymptotic inference about predictive ability”, Econometrica, 64: 1067-1084.

West, Kenneth D. 2006. “Forecast Evaluation”, 100-134, in Handbook of Economic Forecasting, Vol. 1, G. Elliott, C.W.J. Granger and A. Timmerman (eds), Amsterdam: Elsevier.

White, Halbert. 2000. “A Reality Check For Data Snooping”, Econometrica, 68(5): 1097-1126.