The predictive power of Google searches in forecasting unemployment

Francesco D'Amuri and Juri Marcucci

No 891, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: 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.

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: 2012-11
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 (7)

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Journal Article: The predictive power of Google searches in forecasting US unemployment (2017)
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