Factor and Idiosyncratic Empirical Processes
因子成分与特异成分的经验过程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：摘要明确使用大面板近似因子模型，并讨论共同成分分布的结构变化，支持这三个细分方向。
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因子成分与特异成分的经验过程
- 研究问题
- 在大维度近似因子模型中，如何估计单个变量共同成分与特异成分的分布，并构造考虑成分估计误差的同时置信带？
- 方法与思路
- 作者利用大面板中的估计成分构造经验过程，研究维度p与样本量T共同增长下的性质，并据此建立分布函数同时置信带。
- 材料陈述的贡献
- 摘要称在T=o(p)条件下经验过程具有与直接观察成分相当的oracle效率，并报告应用中共同成分分布在2008年危机期间发生变化。
- 阅读关注点
- 应核对除T=o(p)外的因子与依赖条件、成分估计误差何时可忽略、置信带校准，以及应用中分布变化的判断依据。
- 阅读边界
- 索引摘要未注明稿件版本；其余正则条件、具体覆盖率和应用变量未说明，不能仅凭维度关系断言效率或将危机期变化解释为因果效应。
原文摘要
The distributions of the common and idiosyncratic components for an individual variable are important in forecasting and applications. However, they are not identified with low-dimensional observations. Using the recently developed theory for large dimensional approximate factor model for large panel data, the common and idiosyncratic components can be estimated consistently. Based on the estimated common and idiosyncratic components, we construct the empirical processes for estimation of the distribution functions of the common and idiosyncratic components. We prove that the two empirical processes are oracle efficient when T = o(p) where p and T are the dimension and sample size, respectively. This demonstrates that the factor and idiosyncratic empirical processes behave as well as the empirical processes pretending that the common and idiosyncratic components for an individual variable are directly observable. Based on this oracle property, we construct simultaneous confidence bands (SCBs) for the distributions of the common and idiosyncratic components. For the first-order consistency of the estimated distribution functions, T=o(p) suffices. Extensive simulation studies check that the estimated bands have good coverage frequencies. Our real data analysis shows that the common-component distribution has a structural change during the crisis in 2008, while the idiosyncratic-component distribution does not change much. Supplementary materials for this article are available online.