A dynamic factor model approach to incorporate Big Data in state space models for official statistics

Caterina Schiavoni*, Franz Palm, Stephan Smeekes, Jan van den Brakel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)324-353
Number of pages30
JournalJournal of the Royal Statistical Society Series A-Statistics in Society
Issue number1
Early online date9 Nov 2020
Publication statusPublished - Jan 2021


  • factor models
  • Google trends
  • high-dimensional data analysis
  • unemployment
  • nowcasting
  • state space

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