Estimation of domain discontinuities using Hierarchical Bayesian Fay-Herriot models

J.A. van den Brakel*, H.J. Boonstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Changes in the design of a repeated survey generally result in systematic effects in the sample estimates, which are further referred to as discontinuities. To avoid confounding real period-to-period change with the effects of a redesign, discontinuities are often quantified by conducting the old and the new design in parallel for some period of time. Sample sizes of such parallel runs are generally too small to apply direct estimators for domain discontinuities. A bivariate hierarchical Bayesian Fay-Herriot (FH) model is proposed to obtain more precise predictions for domain discontinuities and is applied to a redesign of the Dutch Crime Victimization Survey. This method is compared with a univariate FH model where the direct estimates under the regular approach are used as covariates in a FH model for the alternative approach conducted on a reduced sample size and a univariate FH model where the direct estimates for the discontinuities are modeled directly. An adjusted step forward selection procedure is proposed that minimizes the WAIC until the reduction of the WAIC is smaller than the standard error of this criteria. With this approach more parsimonious models are selected, which prevents selecting complex models that tend to overfit the data.
Original languageEnglish
Pages (from-to)151-189
Number of pages39
JournalSurvey Methodology
Issue number1
Publication statusPublished - 1 Jun 2021


  • Area level models
  • Bivariate Fay-Herriot model
  • Small area estimation
  • Survey redesign
  • Measurement bias
  • MCMC
  • Gibbs sampler

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