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A dynamic factor model approach to incorporate Big Data in state space models for official statistics
Author(s) -
Schiavoni Caterina,
Palm Franz,
Smeekes Stephan,
van den Brakel Jan
Publication year - 2021
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12626
Subject(s) - nowcasting , econometrics , unemployment , dynamic factor , state space representation , state space , multivariate statistics , series (stratigraphy) , estimation , computer science , factor analysis , big data , factor (programming language) , economics , statistics , mathematics , data mining , algorithm , geography , paleontology , management , meteorology , biology , programming language , economic growth
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.