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Statistics for small areas within larger regions are recently required for many economic variables. However, when adding the estimates of the small areas within the larger regions, the results do not match up to those obtained with the appropriate estimator originally derived for the larger region. To avoid discrepancies between estimates benchmarking methods are commonly used in practice. In this paper, we discuss the suitability of using a restricted predictor versus a traditional direct calibrated estimator. The results are illustrated with the 2000 Business Survey of the Basque Country, Spain. [ABSTRACT FROM AUTHOR]

Model-based estimators are becoming very popular in statistical offices because Governments require accurate estimates for small domains that were not planned when the study was designed, as their inclusion would have produced an increase in the cost of the study. The sample sizes in these domains are very small or even zero; consequently, traditional direct design-based estimators lead to unacceptably large standard errors. In this regard, model-based estimators that 'borrow information' from related areas by using auxiliary information are appropriate. This paper reviews, under the model-based approach, a BLUP synthetic and an EBLUP estimator. The goal is to obtain estimators of domain totals when there are several domains with very small sample sizes or without sampled units. We also provide detailed expressions of the mean squared error at different levels of aggregation. The results are illustrated with real data from the Basque Country Business Survey. [ABSTRACT FROM AUTHOR]