Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE approaches is their current inability to deliver reliable measures of uncertainty—and hence confidence intervals—around the small area estimates produced. In this article, we overcome this key limitation via the development of a measure of uncertainty that takes into account both variance and bias, that is, the mean squared error. This new approach is evaluated via a simulation study and demonstrated in a practical application using European Union Statistics on Income and Living Conditions data to explore income levels across Italian municipalities. Evaluations show that the approach proposed delivers accurate estimates of uncertainty and is robust to nonnormal distributions. The approach provides a significant development to widely used spatial microsimulation SAE techniques.

空间微观模拟涵盖了在目标种群参数的小区域估计（SAE）中，从样本调查数据到目标小区域的一系列替代方法学方法，这些环境是需要但没有其他数据的环境。尽管被广泛使用，但空间微观模拟SAE方法的一个持久限制是它们目前无法围绕所产生的小面积估计值提供可靠的不确定性度量，因此也没有置信区间。在本文中，我们通过开发一种同时考虑方差和偏差（即均方误差）的不确定性度量来克服这一关键限制。通过模拟研究对这种新方法进行了评估，并在实际应用中使用了欧盟收入和生活条件统计数据进行了演示，以探索意大利各城市的收入水平。评价表明，所提出的方法可以准确地估计不确定性，并且对非正态分布具有鲁棒性。该方法为广泛使用的空间微观模拟SAE技术提供了重要的发展。