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

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  • Caterina Schiavoni
  • Franz Palm
  • Stephan Smeekes
  • Jan van den Brakel

Abstract

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.

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  • Caterina Schiavoni & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "A dynamic factor model approach to incorporate Big Data in state space models for official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 324-353, January.
    • Handle: RePEc:bla:jorssa:v:184:y:2021:i:1:p:324-353
    DOI: 10.1111/rssa.12626
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    Cited by:

    1. Caterina Schiavoni & Siem Jan Koopman & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "Time-varying state correlations in state space models and their estimation via indirect inference," Tinbergen Institute Discussion Papers 21-020/III, Tinbergen Institute.
    2. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    3. Danny Pfeffermann, 2022. "Time series modelling of repeated survey data for estimation of finite population parameters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1757-1777, October.
    4. Federico Bassetti & Giulia Carallo & Roberto Casarin, 2022. "First-order integer-valued autoregressive processes with Generalized Katz innovations," Papers 2202.02029, arXiv.org.

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