Words are the New Numbers: A Newsy Coincident Index of the Business Cycle
利用日报新闻文本和季度GDP数据，构建每日商业周期指数，能准确识别周期阶段，并在实时预测中与专家判断和组合预测系统竞争，表明新闻能降低经济数据噪声。
I construct a daily business cycle index based on quarterly GDP growth and textual information contained in a daily business newspaper. The newspaper data are decomposed into time series representing news topics, while the business cycle index is estimated using the topics and a time-varying dynamic factor model where dynamic sparsity is enforced upon the factor loadings using a latent threshold mechanism. The resulting index classifies the phases of the business cycle with almost perfect accuracy and provides broad-based high-frequency information about the type of news that drive or reflect economic fluctuations. In out-of-sample nowcasting experiments, the model is competitive with forecast combination systems and expert judgment, and produces forecasts with predictive power for future revisions in GDP. Thus, news reduces noise. Supplementary materials for this article are available online.