Survey Methodology
Estimation of domain discontinuities using Hierarchical Bayesian Fay-Herriot models

by Jan A. van den Brakel and Harm-Jan BoonstraNote 1

  • Release date: June 24, 2021
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Abstract

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.

Key Words:   Area level models; Bivariate Fay-Herriot model; Small area estimation; Survey redesign; Measurement bias; MCMC; Gibbs sampler.

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How to cite

van den Brakel, J.A., and Boonstra, H.-J. (2021). Estimation of domain discontinuities using Hierarchical Bayesian Fay-Herriot models. Survey Methodology, Statistics Canada, Catalogue No. 12‑001‑X, Vol. 47, No. 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2021001/article/00008-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2021

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. The crime victimization survey
Section 3. Methods
Section 4. Results
Section 5. Discussion
Acknowledgements
Appendix
References

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