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

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

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.

• 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, N., and Zimmermann, K. F., (2009), "Google Econometrics and Unemployment Forecasting", IZA Discussion Paper, (4201).

Busetti, F., J. Marcucci, and G. Veronese, (2009), "Comparing Forecast Accuracy: A Monte Carlo Investigation", Bank of Italy, Discussion paper, (723).

Choi, H. and Varian, H. (2009),"Predicting Initial Claims for Unemployment Benefits", Google technical report.

D'Amuri, F., (2009), "Predicting unemployment in short samples with internet job search query data", Bank of Italy, mimeo.

DeLong, J. B., and Summers, L. 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, F.X., and Mariano, R.S., (1995), "Comparing Predictive Accuracy", Journal of Business & Economic Statistics, 13, 253-263.

Elliott, G., T. J., Rothenberg, and J. H. Stock, (1996), "Efficient Tests for an Autoregressive Unit Root", Econometrica, 64, 813-836.

Ginsberg, J. and M. H. Mohebbi and R. S. Patel and L. Brammer and M. S. Smolinski and L. Brilliant, (2009), "Detecting Influenza epidemics using Search Engine Query Data", Nature, 457, 1012-1014.

Golan, A., and J. M., Perloff, (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, P. R., (2005), "A Test for Superior Predictive Ability", Journal of Business and Economic Statistics, 23, 365-380.

Harvey, D.I., Leybourne, S. J. and Newbold, P., (1998), "Tests for Forecast Encompassing", Journal of Business & Economic Statistics, 16, 254-259.

Hastie, T. J., Tibshirani, R. J., (1990) "Generalized Additive Models", Chapman and Hall Ltd., London.

Hubrich, K., and K. D. West, (2009), "Forecast Evaluation of Small Nested Model Sets", ECB Working paper n. 1030.

Koop, G., and S. M. Potter, (1999), "Dynamic Asymmetries in U.S. Unemployment", Journal of Business and Economic Statistics, 17(3), 298-312.

McQueen, G., and Thorley, S., (1993), "Asymmetric Business Cycle Turning Points", Journal of Monetary Economics, 31, 341-362.

Montgomery, A., L., V., Zarnowitz, R. S., Tsay, and, G., C., Tiao, (1998), "Forecasting the U.S. Unemployment Rate", Journal of the American Statistical Association, June, 93(442), 478-493.

Neftci, S. N., (1984), "Are Economic Time Series Asymmetric Over the Business Cycles?", Journal of Political Economy, 85, 281-291.

Politis, D. N., and J. P. Romano, (1994) "The Stationary Bootstrap", Journal of The American Statistical Association, 89(428), 1303-1313.

Proietti, T., (2003), "Forecasting the US Unemployment Rate", Computational Statistics & Data Analysis, 42, 451--476.

Rothman, P., (1998), "Forecasting Asymmetric Unemployment Rates", The Review of Economics and Statistics, February, 80(1), 164-168.

Sichel, D.E., (1993), "Business Cycle Asymmetry: A Deeper Look", Economic Enquiry, 31, 224-236.

Suhoy, T., (2009), "Query Indices and a 2008 Downturn", Bank of Israel Discussion Paper (2009.06).

Wallis, K., (1987), "Time Series Analysis of Bounded Economic Variables", Journal of Time Series Analysis, 8, 115-123.

West, K.D., (1996), "Asymptotic inference about predictive ability", Econometrica, 64, 1067-1084.

West, K.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, H., (2000) "A Reality Check For Data Snooping", Econometrica, 68(5), 1097-1126.