Estimation of level and change for unemployment using structural time series models

Harm Jan Boonstra*, Jan A. van den Brakel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Monthly estimates of provincial unemployment based on the Dutch Labour Force Survey (LFS) are obtained using time series models. The models account for rotation group bias and serial correlation due to the rotating panel design of the LFS. This paper compares two approaches of estimating structural time series models (STM). In the first approach STMs are expressed as state space models, fitted using a Kalman filter and smoother in a frequentist framework. As an alternative, these STMs are expressed as time series multilevel models in an hierarchical Bayesian framework, and estimated using a Gibbs sampler. Monthly unemployment estimates and standard errors based on these models are compared for the twelve provinces of the Netherlands. Pros and cons of the multilevel approach and state space approach are discussed.Multivariate STMs are appropriate to borrow strength over time and space. Modeling the full correlation matrix between time series components rapidly increases the numbers of hyperparameters to be estimated. Modeling common factors is one possibility to obtain more parsimonious models that still account for cross-sectional correlation. In this paper an even more parsimonious approach is proposed, where domains share one overall trend, and have their own independent trends for the domain-specific deviations from this overall trend. The time series modeling approach is particularly appropriate to estimate month-to-month change of unemployment.
Original languageEnglish
Pages (from-to)395-425
Number of pages31
JournalSurvey Methodology
Volume45
Issue number3
Publication statusPublished - Dec 2019

Keywords

  • Small area estimation
  • Structural time series models
  • Time series multilevel models
  • Unemployment estimation
  • SMALL-AREA ESTIMATION
  • ROTATION GROUP BIAS
  • STATE-SPACE MODELS
  • DISTRIBUTIONS
  • PREDICTION
  • COMPONENTS

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