RTFA

Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order tensor time series, and have wide applications in economics, finance and medical imaging. We propose an one-step projection estimator by minimizing the least-square loss function, and further propose a robust estimator with an iterative weighted projection technique by utilizing the Huber loss function. The methods are discussed in Barigozzi et al. (2022) doi:10.48550/arXiv.2206.09800, and Barigozzi et al. (2023) doi:10.48550/arXiv.2303.18163.

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