发表机构
Yale School of Public Health, Yale University; National University of Singapore（耶鲁大学公共卫生学院; 新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合同期协方差与滞后自协方差的高维时间序列因子载荷估计方法，利用HeteroPCA校正异方差噪声，并证明一致性、推导收敛速率，模拟与S&P 500数据验证有效。
AI 中文摘要
因子模型为从高维时间序列中提取共同成分提供了框架。我们开发了一种结合同期协方差与滞后自协方差信息的因子载荷空间估计器。在截面异方差白噪声下，同期协方差包含异质的对角噪声贡献。我们将现有的HeteroPCA算法应用于组合估计矩阵，以校正这种污染，同时保留序列依赖信息。该框架还适应于已知条目集合上的噪声污染，这些条目需满足稀疏性和不相干性条件。在依赖观测的正则性条件下，我们建立了一致性并推导了收敛速率，识别了纳入同期协方差相对于仅用滞后自协方差估计器改善速率的场景。模拟实验评估了载荷空间估计误差以及纳入同期协方差和对角校正的影响。对S&P 500股票收益的应用验证了该方法的有效性。
英文摘要
Factor modeling provides a framework for extracting common components from high-dimensional time series. We develop an estimator of the factor loading space that combines contemporaneous covariance with lagged autocovariance information. Under cross-sectionally heteroskedastic white noise, the contemporaneous covariance contains heterogeneous diagonal noise contributions. We apply the existing HeteroPCA algorithm to the combined estimation matrix to correct this contamination while retaining information from serial dependence. The framework also accommodates noise contamination on a known set of entries subject to sparsity and incoherence conditions. Under regularity conditions for dependent observations, we establish consistency and derive convergence rates, identifying regimes in which incorporating contemporaneous covariance improves the rate relative to the lagged-autocovariance estimator. Simulations evaluate loading-space estimation error and the effects of contemporaneous covariance inclusion and diagonal correction. An application to S\&P 500 stock returns illustrates the method.
