Mosaic inference on panel data
面板数据的马赛克推断AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确针对面板线性回归的聚类依赖、置换检验和置信区间开展推断。
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面板数据的马赛克推断
- 研究问题
- 面板线性回归中不同聚类可能存在隐含依赖时，如何检验聚类独立性，并在不要求完整聚类独立的条件下构造置信区间？
- 方法与思路
- 作者构造与真实误差具有相同不变性的残差，再实施马赛克置换检验；分析局部可交换下的有限样本和聚类独立下的渐近有效性。
- 材料陈述的贡献
- 摘要称方法可检验聚类独立性并构造区间；局部可交换与聚类独立互不蕴含，实验中部分既有方法明显低估方差。
- 阅读关注点
- 应核对残差构造及置换规则、局部可交换性的具体定义，以及有限样本结论与聚类独立下渐近结论分别需要哪些条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；两类条件互不蕴含，不能简单称局部可交换更弱，摘要未说明完整适用条件。
原文摘要
Analysis of panel data via linear regression is widespread across disciplines. To perform statistical inference, such analyses typically assume that clusters of observations are jointly independent. For example, one might assume that observations in New York are independent of observations in New Jersey. Are such assumptions plausible? Might there be hidden dependencies between nearby clusters? This paper introduces a mosaic permutation test that can (i) test the cluster-independence assumption and (ii) produce confidence intervals for linear models without assuming the full cluster-independence assumption. The key idea behind our method is to apply a permutation test to carefully constructed residual estimates that obey the same invariances as the true errors. As a result, our method yields finite-sample valid inferences under a mild "local exchangeability" condition. This condition differs from the typical cluster-independence assumption, as neither assumption implies the other. Furthermore, our method is asymptotically valid under cluster-independence (with no exchangeability assumptions). Together, these results show our method is valid under assumptions that are arguably weaker than the assumptions underlying many classical methods. In experiments on well-studied datasets from the literature, we find that many existing methods produce variance estimates that are up to five times too small, whereas mosaic methods produce reliable results. We implement our methods in the python package mosaicperm.