论文标题
简单的证明大尺寸的近似因素模型
Simpler Proofs for Approximate Factor Models of Large Dimensions
论文作者
论文摘要
近似因素模型的估计值越来越多地用于经验工作。大约二十年前研究的它们的理论特性也为具有横截面依赖性的大尺寸面板数据模型分析了地面工作。本文通过使用替代旋转矩阵，利用低级矩阵的性质以及数据的奇异值分解，除了其协方差结构外，还提供了简化的估计证明。这些简化有助于解释结果，并为该领域的新研究人员提供了更友好的介绍。提供了新的结果，以允许对因子模型施加线性限制。
Estimates of the approximate factor model are increasingly used in empirical work. Their theoretical properties, studied some twenty years ago, also laid the ground work for analysis on large dimensional panel data models with cross-section dependence. This paper presents simplified proofs for the estimates by using alternative rotation matrices, exploiting properties of low rank matrices, as well as the singular value decomposition of the data in addition to its covariance structure. These simplifications facilitate interpretation of results and provide a more friendly introduction to researchers new to the field. New results are provided to allow linear restrictions to be imposed on factor models.
