Survey Methodology
Bayesian small area demography

by Junni L. Zhang, John Bryant and Kirsten NissenNote 1

  • Release date: May 7, 2019
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Abstract

Demographers are facing increasing pressure to disaggregate their estimates and forecasts by characteristics such as region, ethnicity, and income. Traditional demographic methods were designed for large samples, and perform poorly with disaggregated data. Methods based on formal Bayesian statistical models offer better performance. We illustrate with examples from a long-term project to develop Bayesian approaches to demographic estimation and forecasting. In our first example, we estimate mortality rates disaggregated by age and sex for a small population. In our second example, we simultaneously estimate and forecast obesity prevalence disaggregated by age. We conclude by addressing two traditional objections to the use of Bayesian methods in statistical agencies.

Key Words:      Small area estimation; Bayesian hierarchical model; Weakly informative prior; Life expectancy; Obesity; New Zealand; Forecasting.

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How to cite

Zhang, J.L., Bryant, J. and Nissen, K. (2019). Bayesian small area demography. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 45, No. 1. Paper available at https://www150.statcan.gc.ca/n1/pub/12-001-x/2019001/article/00001-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, 2019

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. Mortality rates for Māori
Section 3. Interpolating and forecasting obesity prevalence
Section 4. Discussion
Acknowledgements
Appendix A
Appendix B
References

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