Estimating Bayesian Decision Problems with Heterogeneous Expertise
研究了Iaryczower和Shum提出的两步估计法，发现当数据中共同先验存在变化且个体专业知识存在异质性时，在第一阶段加入个体与时间协变量的交互项能改进结构参数估计，并通过模拟和最高法院数据验证了交互效应的实证重要性。
We consider the recent novel two-step estimator of Iaryczower and Shum (American Economic Review 2012; 102: 202–237), who analyze voting decisions of US Supreme Court justices. Motivated by the underlying theoretical voting model, we suggest that where the data under consideration display variation in the common prior, estimates of the structural parameters based on their methodology should generally benefit from including interaction terms between individual and time covariates in the first stage whenever there is individual heterogeneity in expertise. We show numerically, via simulation and re-estimation of the US Supreme Court data, that the first-order interaction effects that appear in the theoretical model can have an important empirical implication. Copyright © 2015 John Wiley & Sons, Ltd.