发表机构
University at Albany, State University of New York; University of Connecticut（纽约州立大学奥尔巴尼分校; 康涅狄格大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究随机分配回归元下自助推断的失效问题，提出仅需恢复随机化回归元得分携带的协方差，并通过标准依赖估计器在有序或分组依赖条件下恢复有效性，模拟表明固定带宽校准可减少尺寸失真。
AI 中文摘要
随机分配可以使普通最小二乘（OLS）推断对结果依赖性不敏感，但独立同分布（iid）重抽样仍可能失败，因为分配和重抽样未必能消除相同的协方差项。在二元处理情形下，iid方差目标与抽样方差之差恰好等于处理效应的负的跨单元总协方差。在高斯一阶极限下，正协方差导致过度拒绝，负协方差导致拒绝不足。两种设计可以生成每个观测的相同分布，但需要符号相反的方差校正，因此仅基于边际的方差校正无法对两者都达到一阶精确。对于一阶高斯推断，只需恢复一个协方差分量——由随机化回归元得分所携带的协方差。对该得分采用标准依赖估计器，在适当的有序或分组依赖条件下可恢复有效性。在有序数据的模拟中，固定带宽校准减少了若干大带宽的尺寸失真。
英文摘要
Random assignment can make ordinary least squares (OLS) inference insensitive to outcome dependence, yet iid resampling can still fail because assignment and resampling need not remove the same covariance terms. With binary treatment, the iid variance target differs from the sampling variance by exactly minus aggregate cross-unit covariance of treatment effects. Under a Gaussian first-order limit, positive covariance leads to over-rejection and negative covariance to under-rejection. Two designs can generate the same distribution for each observation but require variance corrections of opposite signs, so no marginal-only variance correction is first-order exact for both. For first-order Gaussian inference, only one covariance component---the covariance carried by the randomized-regressor score---must be recovered. Standard dependence estimators on that score restore validity under suitable ordered or grouped dependence conditions. In simulations with ordered data, fixed-bandwidth calibration reduces several large-bandwidth size distortions.
