K. Aruma Rao

Methods for small area estimation have received much attention in recent years due to growing demand for reliable small area statistics that are needed in formulating policies and programs, allocation of government funds, making business decisions and so on. Traditional area-specific direct estimation methods are not suitable in the small area context because of small (or even zero) area-specific sample sizes. As a result, indirect estimation methods that borrow information across related areas through implicit or explicit linking models and auxiliary information, such as census data and administrative records, are needed. This paper provides an introduction to small area estimation with emphasis on explicit model-based estimation. Methods covered include «off-the-shelf» re-weighting methods, simulated census methods used by the World Bank and formal empirical Bayes and hierarchical Bayes methods, based on explicit models. Formal model-based methods permit the estimation of mean squared prediction error and the construction of confidence intervals.