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时间效应序列相关下双向聚类的刀切法方差估计AI 解读

Chiang, Hansen, and Sasaki (2024) and Chen and Vogelsang (2024) developed cluster-robust variance estimators (CRVEs) for linear regressions with two-way clustered data allowing for arbitrary serial dependence. However, conventional CRVEs can perform poorly in finite samples. We propose jackknife CRVEs and establish their theoretical validity. Our theoretical and simulation results demonstrate several advantages of the proposed procedures. First, the jackknife CRVE delivers…

多维聚类与序列相关时间效应下的野生自助法推断AI 解读

This article studies wild bootstrap-based inference for regression models with multiway clustering. Our proposed methods are multiway counterparts to the (one-way) wild cluster bootstrap approach introduced by Cameron, Gelbach, and Miller. We establish the validity of our methods for studentized statistics. Theoretical results are provided, accommodating arbitrary serial dependence in the common time effects—an aspect excluded by existing two-way bootstrap-based approaches.…