The predictive power of Google searches in forecasting US unemployment

摘要

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. (C) 2017 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.