Unemployment rate forecasting using Google trends

In order to make sound economic decisions, it is of great importance to be able to predict and interpret macro-economic variables. Researchers are therefore seeking continuously to improve the prediction performance. One of the main economic indicators is the US unemployment rate. In this paper, we empirically analyze whether, and to what extent, Google search data have additional predictive power in forecasting the US unemployment rate. This research consists of two parts. First, we look for and select Google search data with potential predicitive power. Second, we evaluate the performance of level and directional forecasts. Here, we make use of different models, based on both econometric and machine learning techniques. We find that Google trends improve the predictive accuracy in all used forecasting methods. Lastly, we discuss the limitations of our research and possible future research suggestions.