We propose a way to formalize the relationship between descriptive analysis and structural estimation. A researcher reports an estimate c of a structural quantity of interest c that is valid under some model. The researcher also reports descriptive statistics ˆ? that estimate features ? of the distribution of the data, and highlights the economic relationship between ? and c under the model. We compare the bound on the absolute bias of c across all models in a local neighborhood of the assumed model with the bound across a subset of these models under which the assumed relationship between ? and c is correct. Our main result shows that the ratio of these tight bounds depends only on a quantity we call the informativeness of ˆ? for c. Informativeness can be easily estimated even for complex models. We recommend that researchers report estimated informativeness alongside their descriptive analyses, and we illustrate with applications to three recent papers.

中文翻译：

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关于结构估计的描述性统计的信息性

我们提出了一种形式化描述性分析和结构估计之间关系的方法。研究人员报告了在某些模型下有效的感兴趣结构量 c 的估计 c。研究人员还报告了描述性统计数据 ˆ? 那估计特征？的分布，并突出了之间的经济关系 ? 和 c 在模型下。我们将假设模型的局部邻域中所有模型的 c 的绝对偏差的界限与这些模型的子集的界限进行比较，在该界限下， ? 和 c 是正确的。我们的主要结果表明，这些严格界限的比率仅取决于我们称为 ˆ? 对于 c. 即使对于复杂的模型，也可以轻松估计信息量。