Small area estimation combining information from several sources

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Jae-kwang Kim, Seunghwan Park and Seo-young Kim^Note 1

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Abstract

An area-level model approach to combining information from several sources is considered in the context of small area estimation. At each small area, several estimates are computed and linked through a system of structural error models. The best linear unbiased predictor of the small area parameter can be computed by the general least squares method. Parameters in the structural error models are estimated using the theory of measurement error models. Estimation of mean squared errors is also discussed. The proposed method is applied to the real problem of labor force surveys in Korea.

Key Words: Area-level model; Auxiliary information; Measurement error models; Structural error model; Survey integration.

Table of content

• 1. Introduction
• 2. Basic theory
• 3. Parameter estimation
• 4. MSE estimation
• 5. Application to Korean Labor Force survey
• 6. Concluding remark
• Acknowledgements
• Appendix
• References

Notes