Two applications of wild bootstrap methods to improve inference in cluster‐IV models
针对微观数据中聚类依赖、工具变量弱且聚类数少时检验偏大的问题，提出两种野自助法变体，显著减小了绝对规模偏差，建议将其纳入标准工具变量和聚类模型的分析工具包。
Summary Microeconomic data often have within‐cluster dependence, which affects standard error estimation and inference. When the number of clusters is small, asymptotic tests can be severely oversized. In the instrumental variables (IV) model, the potential presence of weak instruments further complicates hypothesis testing. We use wild bootstrap methods to improve inference in two empirical applications with these characteristics. Building from estimating equations and residual bootstraps, we identify variants robust to the presence of weak instruments and a small number of clusters. They reduce absolute size bias significantly and demonstrate that the wild bootstrap should join the standard toolkit in IV and cluster‐dependent models.