We make use of Google search data in an attempt to predict unemployment, CPI and consumer confidence for the US, UK, Canada, Germany and Japan. Google search queries have previously proven valuable in predicting macroeconomic variables in an in-sample context. However, to the best of our knowledge, the more challenging question of whether such data have out-of-sample predictive value has not yet been answered satisfactorily. We focus on out-of-sample nowcasting, and extend the Bayesian structural time series model using the Hamiltonian sampler for variable selection. We find that the search data retain their value in an out-of-sample predictive context for unemployment, but not for CPI or consumer confidence. It is possible that online search behaviours are a relatively reliable gauge of an individual’s personal situation (employment status), but less reliable when it comes to variables that are unknown to the individual (CPI) or too general to be linked to specific search terms (consumer confidence).

Bayesian methods, forecasting practice, Kalman filter, macroeconomic forecasting, state space models, nowcasting, spike-and-slab, Hamiltonian sampler
dx.doi.org/10.1016/j.ijforecast.2018.12.006, hdl.handle.net/1765/115743
International Journal of Forecasting
Department of Econometrics

Niesert, R.F, Oorschot, J.A, Veldhuisen, C.P, Brons, K, & Lange, R.-J. (2019). Can Google search Data help predict macroeconomic series?. International Journal of Forecasting. doi:10.1016/j.ijforecast.2018.12.006