Factor Augmented Matrix Regression

Journal of the American Statistical Association · Volume NaN

DOI: 10.1080/01621459.2025.2595734

Abstract

We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, utilizing techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm offers an accelerated solution for penalized matrix factor regression. Both algorithms are supported by established statistical and numerical convergence properties. Empirical evaluations conducted on synthetic and real economics datasets demonstrate FAMAR’s superiority in terms of accuracy, interpretability, and computational speed. An application to economic data showcases how matrix factors can be incorporated to predict the GDPs of the countries of interest and the influence of these factors on the GDPs.

Keywords

Matrix factor models, Matrix regression, Factor-augmented regression, Diversified projections, High-dimensionality

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