In this paper we consider estimation of unobserved components in state space models using a dynamic factor approach to incorporate auxiliary information from high‐dimensional data sources. We apply the methodology to unemployment estimation as done by Statistics Netherlands, who uses a multivariate state space model to produce monthly figures for unemployment using series observed with the labour force survey (LFS). We extend the model by including auxiliary series of Google Trends about job‐search and economic uncertainty, and claimant counts, partially observed at higher frequencies. Our factor model allows for nowcasting the variable of interest, providing reliable unemployment estimates in real‐time before LFS data become available.

中文翻译：

---

一种动态因素模型方法，可将大数据合并到用于官方统计的状态空间模型中

在本文中，我们考虑使用动态因子方法并结合来自高维数据源的辅助信息来估计状态空间模型中未观察到的分量。正如荷兰统计局所做的那样，我们将该方法应用于失业估计，后者使用多变量状态空间模型，通过劳动力调查（LFS）观察到的序列，得出每月的失业数字。我们通过包括有关工作搜索和经济不确定性以及索赔人数量的Google趋势辅助系列（部分在较高频率下观察到）来扩展模型。我们的因子模型允许即时预测感兴趣的变量，在LFS数据可用之前实时提供可靠的失业估计。