Forecasting GDP Growth Rates Using Google Trends in the United States and Brazil

In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed (U.S.) and emerging-market economy (Brazil). Our focus is on the marginal contribution of "Big Data" in the form of Google Trends data over and above that of traditional predictors, and we use a dynamic factor model to handle the large number of potential predictors and the "ragged-edge" problem. We find that factor models based on economic indicators and Google "categories" data provide significant gains compared to models that exclude this information. Using more disaggregated Google Trends data than its "categories" is not helpful. The benefits of using Google Trends data are greater for the U.S. than Brazil