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潜在多维聚类：推断与实践AI 解读

We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their…

政策相关处理参数对单调性违背的敏感性AI 解读

ABSTRACT This paper proposes a method to bound policy‐relevant treatment parameters (PRTPs) when the monotonicity assumption that the IV affects individuals' treatment response in the same direction is weakened. The bounding framework uses the proportion of defiers relative to compliers as a sensitivity parameter and yields an identified set that is an interval. The method is illustrated in an empirical application where the same‐sex instrument was used to calculate the…

双向聚类的渐近理论AI 解读

This paper proves a new central limit theorem for a sample that exhibits two-way dependence and heterogeneity across clusters. Statistical inference for situations with both two-way dependence and cluster heterogeneity has thus far been an open issue. The existing theory for two-way clustering inference requires identical distributions across clusters (implied by the so-called separate exchangeability assumption). Yet no such homogeneity requirement is needed in the existing…