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Factor models for matrix-valued high-dimensional time series
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DOI：10.1016/j.jeconom.2018.09.013.png)
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摘要
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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.
Keyword:
FACE REPRESENTATION
VALUE DECOMPOSITION
2-DIMENSIONAL PCA
LATENT FACTORS
NUMBER
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