The University of Georgia
Random effects models play an important role in model-based small area estimation. Random effects account for any lack of fit of a regression model for the population means of small areas on a set of explanatory variables. In Datta, Hall and Mandal (2011, JASA), we showed that if the random effects can be dispensed with through a statistical test, then the model parameters and the small area means can be estimated substantially accurately. This work is most useful when the number of small areas, m, is moderately large.