Survey Methodology
Performance of hierarchical Bayes small area estimators using noninformative and informative priors with an application to the Canadian Labor Force Survey

by Yong You and Keven BosaNote 1

  • Release date: December 23, 2025
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Abstract

In this paper, we study the performance of hierarchical Bayes (HB) small area estimators using noninformative and informative priors. We apply the Bayesian models of You and Chapman (2006) and You (2021) to the Canadian Labor Force Survey (LFS) data and evaluate the impact of the priors on the HB estimators. A Bayesian model comparison and simulation study are also conducted. Our results indicate that a correct informative prior can lead to very good results, and noninformative priors can also perform very well. Incorrect informative priors can lead to poor results in terms of large bias and large coefficient of variation (CV). Noninformative priors are recommended in practice for HB small area estimation unless correctly specified informative priors are available. Informative priors are particularly useful when the number of small areas is relatively small.

Key Words:    Bias; CPO; Fay-Herriot model; Gibbs sampling; Model selection; Posterior predictive distribution.

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How to cite

You, Y. and Bosa, K. (2025). Performance of hierarchical Bayes small area estimators using noninformative and informative priors with an application to the Canadian Labor Force Survey. Survey Methodology, 51(2), 425-448. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2025002/article/00010-eng.pdf.

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ISSN : 1492-0921

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Published by authority of the Minister responsible for Statistics Canada.

© His Majesty the King in Right of Canada, as represented by the Minister of Industry, 2025

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Catalogue No. 12-001-X

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