论文标题
使用Google趋势数据进行现代增长：贝叶斯结构时间序列模型
Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model
论文作者
论文摘要
本文以Google趋势的形式调查了互联网搜索数据的好处，以通过混合频率贝叶斯结构时间序列（BSTS）模型实时实时实时增长。我们增强并增强了模型和方法，以使这些模型和方法更适合使用大量潜在的协变量。具体而言，我们允许将状态方差缩小到零以避免过度拟合，在更灵活的正常内gamma之前延长SSV（尖峰和平板变量选择），这在BST之前保持了对基础模型大小的不可知论，并适应了hors虫。 Nowcast GDP增长以及模拟研究的应用表明，在SSV和原始BSTS模型上，Horseshoe Prior BST在密集的数据生成过程中取得了最大的收益。我们的应用程序还表明，在其他宏观经济数据可用之前，一组较大的搜索词可以在特定季度的早期进行改进。具有较高纳入概率的搜索词具有良好的经济解释，反映了经济焦虑和财富影响的主要信号。
This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normal-inverse-gamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense data-generating-processes. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects.
