Predicting the present with search engine data

Abstract

Many businesses now have almost real time data available about their operations. This data can be helpful in contemporaneous prediction ("nowcasting") of various economic indicators. We illustrate how one can use Google search data to nowcast economic metrics of interest, and discuss some of the ramifications for research and policy. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We use Kalman filtering to whiten the time series in question by removing the trend and seasonal behavior. Spike-and-slab regression is a Bayesian method for variable selection that works even in cases where the number of predictors is far larger than the number of observations. Finally, we use Markov Chain Monte Carlo methods to sample from the posterior distribution for our model; the final forecast is an average over thousands of draws from the posterior. An advantage of the Bayesian approach is that it allows us to specify informative priors that affect the number and type of predictors in a flexible way.