Summary Small area estimation is a research area in official and survey statistics of great practical relevance for national statistical institutes and related organizations. Despite rapid developments in methodology and software, researchers and users would benefit from having practical guidelines for the process of small area estimation. We propose a general framework for the production of small area statistics that is governed by the principle of parsimony and is based on three broadly defined stages, namely specification, analysis and adaptation, and evaluation. Emphasis is given to the interaction between a user of small area statistics and the statistician in specifying the target geography and parameters in the light of the available data. Model‐free and model‐dependent methods are described with a focus on model selection and testing, model diagnostics and adaptations such as use of data transformations. Uncertainty measures and the use of model and design‐based simulations for method evaluation are also at the centre of the paper. We illustrate the application of the proposed framework by using real data for the estimation of non‐linear deprivation indicators. Linear statistics, e.g. averages, are included as special cases of the general framework.
This paper addresses the problem of producing small area estimates of Ethnicity by Local Authority in England. A Structure Preserving approach is proposed, making use of the Generalized Structure Preserving Estimator. In order to identify the best way to use the available aggregate information, three fixed effects models with increasing levels of complexity were tested. Finite Population Mean Square Errors were estimated using a bootstrap approach. However, more complex models did not perform substantially better than simpler ones. A mixed-effects approach does not seem suitable for this particular application because of the very small sample sizes observed in many areas. Further research on a more flexible fixed-effects estimator is proposed.
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