On the Informativeness of Descriptive Statistics for Structural Estimates

• 成果类型: Article
• 署名作者: Andrews, Isaiah; Gentzkow, Matthew; Shapiro, Jesse M.
• 署名单位: Harvard University; National Bureau of Economic Research; Stanford University; Brown University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA16768
• 发表日期: 2020
• 页码: 2231-2258
• 关键词: model

摘要

We propose a way to formalize the relationship between descriptive analysis and structural estimation. A researcher reports an estimate (c) over cap of a structural quantity of interest c that is exactly or asymptotically unbiased under some base model. The researcher also reports descriptive statistics (gamma) over cap that estimate features gamma of the distribution of the data that are related to c under the base model. A reader entertains a less restrictive model that is local to the base model, under which the estimate (c) over cap may be biased. We study the reduction in worst-case bias from a restriction that requires the reader's model to respect the relationship between c and gamma specified by the base model. Our main result shows that the proportional reduction in worst-case bias depends only on a quantity we call the informativeness of (gamma) over cap for (c) over cap. 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.