  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 the 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.
