Forecasting GDP growth rates in the United States and Brazil using Google Trends
研究了谷歌趋势数据对预测美国和巴西GDP增长的边际贡献，发现结合经济指标和谷歌分类数据的因子模型优于传统模型，但更细化的数据无益。
In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed economy (the U.S.) and an 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 gains compared to models that exclude this information. The benefits of using Google Trends data appear to be broadly similar for Brazil and the U.S., and depend on the factor model variable-selection strategy. Using more disaggregated Google Trends data than its “categories” is not beneficial.