Forecasting Unemployment with Google Searches - Joonas Tuhkuri (The Research Institute of the Finnish Economy (ETLA), 2016)
Data on Google searches help predict the unemployment rate in the U.S. But the predictive power of Google searches is limited to short-term predictions, the value of Google data for forecasting purposes is episodic, and the improvements in forecasting accuracy are only modest. The results, obtained by (pseudo) out-of-sample forecast comparison, are robust to a state-level fixed effects model and to different search terms. Joint analysis by cross-correlation function and Granger non-causality tests verifies that Google searches anticipate the unemployment rate. The results illustrate both the potentials and limitations of using big data to predict economic indicators.
Keywords: C22; C53; C55; C82; E27; 330; Big Data; Google; Internet; Nowcasting; Forecasting; Unemployment