Purchase data from retail chains can provide proxy measures of private household expenditure on items that are the most troublesome to collect in the traditional expenditure survey. Due to the inevitable coverage and selection errors, bias must exist in these proxy measures. Moreover, given the sheer amount of data, the bias completely dominates the variance. To investigate the potential of replacing costly and burdensome surveys by non‐survey big‐data sources, we propose an audit sampling inference approach, which does not require linking the audit sample and the big‐data source at the individual level. It turns out that one is unable to reject a null hypothesis of unbiased big‐data estimation at the chosen size, because the audit sampling variance is too large compared to the bias of the big‐data estimate. For the same reason, audit sampling fails to yield a meaningful mean squared error estimate. We propose a novel accuracy measure that is generally applicable in such situations. This can provide a necessary part of the statistical argument for the uptake of non‐survey big‐data sources, in replacement of traditional survey sampling. An application to disaggregated food price indices is used to demonstrate the proposed approach.

中文翻译：

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消费者物价指数的代理支出权重：大数据统计的审计抽样推论

来自零售链的购买数据可以提供私人家庭支出的代理指标，这些支出是传统支出调查中最难收集的项目。由于不可避免的覆盖和选择错误，这些代理措施中必须存在偏见。而且，鉴于庞大的数据量，偏差完全支配了方差。调查替代的潜力通过非调查性大数据源进行的昂贵且繁琐的调查，我们提出了一种审计抽样推断方法，该方法不需要将审计样本与大数据源在各个级别上进行链接。事实证明，在选定的大小下，无法拒绝无偏大数据估计的零假设，因为与大数据估计的偏差相比，审计抽样方差太大。出于同样的原因，审计抽样无法得出有意义的均方误差估计。我们提出了一种新颖的精度度量，该度量通常适用于这种情况。这可以为使用非调查大数据源提供统计论证的必要部分，以代替传统的调查抽样。用于分类食品价格指数的应用被用来证明所提出的方法。