We propose small area estimators of general indicators in off-census years, which avoid the use of deprecated census microdata, but are nearly optimal in census years. The procedure is based on replacing the obsolete census file with a larger unit-level survey that adequately covers the areas of interest and contains the values of useful auxiliary variables. However, the minimal data requirement of the proposed method is a single survey with microdata on the target variable and suitable auxiliary variables for the period of interest. We also develop an estimator of the mean squared error (MSE) that accounts for the uncertainty introduced by the large survey used to replace the census of auxiliary information. Our empirical results indicate that the proposed predictors perform clearly better than the alternative predictors when census data are outdated, and are very close to optimal ones when census data are correct. They also illustrate that the proposed total MSE estimator corrects for the bias of purely model-based MSE estimators that do not account for the large survey uncertainty.

翻译：我们提出了一种用于非普查年份通用指标的小区域估计方法，该方法避免使用过时的普查微观数据，同时在普查年份接近最优。该程序基于用更大规模的单位层面调查替代过时的普查文件，该调查需充分覆盖目标区域并包含有用辅助变量的数值。然而，所提方法的最低数据要求是：在目标时期内，仅需一项包含目标变量微观数据及合适辅助变量的调查。我们还开发了一种均方误差估计量，该估计量考虑了因使用大规模调查替代辅助信息普查所引入的不确定性。实证结果表明：当普查数据过时时，所提预测因子的性能明显优于替代预测因子；当普查数据准确时，其性能非常接近最优预测因子。结果还表明，所提出的总均方误差估计量能够修正纯模型化均方误差估计量的偏差，后者未考虑大规模调查的不确定性。