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回应讨论
We thank the discussants for their insightful comments. We agree with many of the points they raise, and especially welcome their thoughts on directions for future work. Here, we elaborate one point that these discussions led us to think more about. As Bonhomme notes, the audience plays a central role in our analysis, and as all discussants note, so does the nature of disagreement and uncertainty about the researcher’s assumptions. Some assumptions are what Leamer (1983)…
结构研究中的透明性
We propose a formal definition of transparency in empirical research and apply it to structural estimation in economics. We discuss how some existing practices can be understood as attempts to improve transparency, and we suggest ways to improve current practice, emphasizing approaches that impose a minimal computational burden on the researcher. We illustrate with examples.
描述性统计对结构估计的信息含量
We propose a way to formalize the relationship between descriptive analysis and structural estimation. A researcher reports an estimate ĉ of a structural quantity of interest c that is exactly or asymptotically unbiased under some base model. The researcher also reports descriptive statistics γ ˆ that estimate features γ 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,…
高维选择中的群体差异测度：方法及国会演讲应用
We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We show that standard approaches suffer from a severe finite‐sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a…