An applied comparison of area-level linear mixed models in small area estimation

Luis Nobre Pereira, Pedro Simões Coelho

Research output: Contribution to journalReview articlepeer-review


This article reviews four area-level linear mixed models that borrow strength by exploiting the possible correlation among the neighboring areas or/and past time periods. Its main goal is to study if there are efficiency gains when a spatial dependence or/and a temporal autocorrelation among random-area effects are included into the models. The Fay-Herriot estimator is used as benchmark. A design-based simulation study based on real data collected from a longitudinal survey conducted by a statistical office is presented. Our results show that models that explore both spatial and chronological association considerably improve the efficiency of small area estimates.

Original languageEnglish
Pages (from-to)671-685
Number of pages15
JournalCommunications in Statistics: Simulation and Computation
Issue number3
Publication statusPublished - 1 Jan 2013


  • Empirical best linear unbiased prediction
  • Linear mixed model
  • Small area estimation
  • Spatial correlation
  • Temporal autocorrelation


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