摘要翻译：
对近似因子模型的估计越来越多地用于经验工作。它们的理论性质在二十多年前就得到了研究，这也为分析具有截面相关性的大维面板数据模型奠定了基础。利用低秩矩阵的性质，以及数据的奇异值分解及其协方差结构，利用交替旋转矩阵给出了估计的简化证明。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，允许对因子模型施加线性限制。
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英文标题：
《Simpler Proofs for Approximate Factor Models of Large Dimensions》
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作者：
Jushan Bai and Serena Ng
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/2008.00254