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The predictive power of Google searches in forecasting unemployment

  • Francesco D'Amuri

    ()

    (Bank of Italy)

  • Juri Marcucci

    ()

    (Bank of Italy)

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.

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File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2012/2012-0891/en_tema_891.pdf
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Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 891.

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Date of creation: Nov 2012
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Handle: RePEc:bdi:wptemi:td_891_12
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Web page: http://www.bancaditalia.it

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