English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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