The Predictive Power of Google Searches in Forecasting Unemployment

57 Pages Posted: 29 Jan 2013

Date Written: November 29, 2012

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

JEL Classification: C22, C53, E27, E37, J60, J64

Suggested Citation

D’Amuri, Francesco and Marcucci, Juri, The Predictive Power of Google Searches in Forecasting Unemployment (November 29, 2012). Bank of Italy Temi di Discussione (Working Paper) No. 891, Available at SSRN: https://ssrn.com/abstract=2207915 or http://dx.doi.org/10.2139/ssrn.2207915

Francesco D’Amuri (Contact Author)

Bank of Italy ( email )

Via Nazionale 91
00184 Roma
Italy

Juri Marcucci

Bank of Italy ( email )

Via Nazionale , 91
Rome, 00184
Italy
+39-06-4792-4069 (Phone)

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