Factor models for matrix-valued high-dimensional time series
Factor models for matrix-valued high-dimensional time series
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DOI：
10.1016/j.jeconom.2018.09.013
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发表时间：
2019-01-01
影响因子：
6.3
通讯作者：
Chen, Rong
中科院分区：
文献类型：
--
作者：
Wang, Dong;Liu, Xialu;Chen, Rong
在金融、经济和许多其他领域，经常会观察到矩阵形式的观察结果。例如，随着时间的推移，许多经济指标是在不同的国家获得的。随着时间的推移，许多公司的各种财务特征都会得到报告。虽然将矩阵观测转换为长向量，然后使用标准向量时间序列模型或因子分析是很自然的，但通常情况下，矩阵的列和行表示以非常结构化的方式密切相关的不同信息集。我们提出了一种新的因子模型，该模型保持和利用矩阵结构来实现更大的降维，并找到更清晰和更可解释的因子结构。对估计过程及其理论性质进行了研究，并用仿真和实例进行了验证。(C)2018爱思唯尔B.V.保留所有权利。
In finance, economics and many other fields, observations in a matrix form are often observed over time. For example, many economic indicators are obtained in different countries over time. Various financial characteristics of many companies are reported over time. Although it is natural to turn a matrix observation into a long vector then use standard vector time series models or factor analysis, it is often the case that the columns and rows of a matrix represent different sets of information that are closely interrelated in a very structural way. We propose a novel factor model that maintains and utilizes the matrix structure to achieve greater dimensional reduction as well as finding clearer and more interpretable factor structures. Estimation procedure and its theoretical properties are investigated and demonstrated with simulated and real examples. (C) 2018 Elsevier B.V. All rights reserved.