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

D'Amuri, Francesco/FD and Juri Marcucci

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Google econometrics; Forecast comparison; Keyword search; US unemployment; Time series models. (search for similar items in EconPapers)
JEL-codes: C22 C53 E27 E37 J60 J64 (search for similar items in EconPapers)
Date: 2009-10-30
New Economics Papers: this item is included in nep-for and nep-lab
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

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https://mpra.ub.uni-muenchen.de/18248/1/MPRA_paper_18248.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/18732/3/MPRA_paper_18732.pdf revised version (application/pdf)

Related works:
Working Paper: “Google it!”Forecasting the US Unemployment Rate with a Google Job Search index (2010)
Working Paper: ‘Google it!’ Forecasting the US unemployment rate with a Google job search index (2009)
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