Spatial weight matrices play a key role in econometrics to capture spatial effects. However, these constructs are prone to clustering and can be challenging to analyse in common statistical packages such as STATA. Multiple observations of survey participants in the same location (or cluster) have traditionally not been dealt with appropriately by statistical packages. It is common that participants are assigned Geographic Information System (GIS) data at a regional or district level rather than at a small area level. For example, the Demographic Health Survey (DHS) generates GIS data at a cluster level, such as a regional or district level, rather than providing coordinates for each participant. Moreover, current statistical packages are not suitable for estimating large matrices such as 20,000 × 20,000 (reflective of data within large health surveys) since the statistical package limits the N to a smaller number. In addition, in many cases, GIS information is offered at an aggregated level of geographical areas. To alleviate this problem, this paper proposes a bootstrap approach that generates an inverse distance spatial weight matrix for application in econometric analyses of health survey data. The new approach is illustrated using DHS data on uptake of HIV testing in low and middle income countries.

中文翻译：

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当多个观察值在大型健康调查中具有相同位置时，用于生成距离反比权重矩阵的自举方法。

空间权重矩阵在计量经济学中发挥重要作用，以捕获空间效应。但是，这些结构易于聚类，并且在诸如STATA之类的常见统计数据包中进行分析可能具有挑战性。传统上，统计软件包无法适当地处理在同一位置（或群集）的调查参与者的多次观察。通常会在区域或地区级别而不是在小范围级别为参与者分配地理信息系统（GIS）数据。例如，人口健康调查（DHS）在集群级别（例如区域或地区级别）生成GIS数据，而不是为每个参与者提供坐标。而且，当前的统计数据包不适合估算20,000×20，000（反映大型健康调查中的数据），因为统计数据包将N限制为较小的数字。此外，在许多情况下，GIS信息是在汇总的地理区域级别上提供的。为了缓解这个问题，本文提出了一种自举方法，该方法生成逆距离空间权重矩阵，以用于健康调查数据的计量分析。使用DHS数据说明了中低收入国家接受HIV检测的新方法。本文提出了一种自举方法，该方法可生成反距离空间权重矩阵，以用于健康调查数据的计量分析。使用DHS数据说明了中低收入国家接受HIV检测的新方法。本文提出了一种自举方法，该方法可生成反距离空间权重矩阵，以用于健康调查数据的计量分析。使用DHS数据说明了中低收入国家接受HIV检测的新方法。