Reworking wild bootstrap‐based inference for clustered errors
针对聚类数据中野聚类自举法推断的模糊性问题，提出了新的6点自举权重分布和核密度估计方法，蒙特卡洛模拟表明这些改进能提高推断可靠性。
Abstract Cluster‐robust inference is increasingly common in empirical research. With few clusters, inference is often conducted using the wild cluster bootstrap. With conventional bootstrap weights the set of valid ‐values can create ambiguities in inference. I consider several modifications to the bootstrap procedure to resolve these ambiguities. Monte Carlo simulations provide evidence that both a new 6‐point bootstrap weight distribution and a kernel density estimation approach improve the reliability of inference. A brief empirical example highlights the implications of these findings.