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The predictive power of Google searches in forecasting US unemployment

D’Amuri, Francesco and Juri Marcucci
Authors registered in the RePEc Author Service: Francesco D'Amuri ()

International Journal of Forecasting, 2017, vol. 33, issue 4, 801-816

Abstract: We assess the performance of an index of Google job-search intensity as a leading indicator for predicting the monthly US unemployment rate. We carry out a deep out-of-sample forecasting comparison of models that adopt the Google Index, the more standard initial claims, or alternative indicators based on economic policy uncertainty and consumers’ and employers’ surveys. The Google-based models outperform most of the others, with their relative performances improving with the forecast horizon. Only models that use employers’ expectations on a longer sample do better at short horizons. Furthermore, quarterly predictions constructed using Google-based models provide forecasts that are more accurate than those from the Survey of Professional Forecasters, models based on labor force flows, or standard nonlinear models. Google-based models seem to predict particularly well at the turning point that takes place at the beginning of the Great Recession, while their relative predictive abilities stabilize afterwards.

Keywords: Google econometrics; Forecast comparison; Keyword search; US unemployment; Time series models (search for similar items in EconPapers)
Date: 2017
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Working Paper: The predictive power of Google searches in forecasting unemployment (2012) Downloads
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