摘要翻译：
我们考虑了时间序列数据的近似因子模型的估计，其中特殊成分之间存在强烈的序列和横截面相关性。这种设置在许多应用中自然出现，但文献中现有的方法依赖于这样的假设，即这种相关性是弱的，导致选择的因素数量的错误描述，从而导致不准确的推断。在本文中，我们通过观察到的多变量时间序列的滞后值显式地结合了存在于特质成分中的依赖结构。提出了一个同时估计因子空间和滞后值转移矩阵{\em}的约束优化问题，其中的约束反映了公因子的低秩性和转移矩阵的稀疏性。我们建立了所得估计的理论性质，并介绍了一个易于实现的经验工作的计算过程。利用综合数据对模型和实施程序的性能进行了评价，并与竞争方法进行了比较，并以美国75家大型金融机构2001-2016年的每周对数报表为例进行了进一步说明。
---
英文标题：
《Approximate Factor Models with Strongly Correlated Idiosyncratic Errors》
---
作者：
Jiahe Lin, George Michailidis
---
最新提交年份：
2019
---
分类信息：

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
--
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。
--

---
英文摘要：
  We consider the estimation of approximate factor models for time series data, where strong serial and cross-sectional correlations amongst the idiosyncratic component are present. This setting comes up naturally in many applications, but existing approaches in the literature rely on the assumption that such correlations are weak, leading to mis-specification of the number of factors selected and consequently inaccurate inference. In this paper, we explicitly incorporate the dependent structure present in the idiosyncratic component through lagged values of the observed multivariate time series. We formulate a constrained optimization problem to estimate the factor space and the transition matrices of the lagged values {\em simultaneously}, wherein the constraints reflect the low rank nature of the common factors and the sparsity of the transition matrices. We establish theoretical properties of the obtained estimates, and introduce an easy-to-implement computational procedure for empirical work. The performance of the model and the implementation procedure is evaluated on synthetic data and compared with competing approaches, and further illustrated on a data set involving weekly log-returns of 75 US large financial institutions for the 2001-2016 period.
---
PDF链接：
https://arxiv.org/pdf/1912.04123