Statistical modeling study demonstrates a unified matrix autoregressive framework for high-dimensional data, highlighting improved efficiency in global macroeconomic forecasting.
Key Points
To develop a unified modeling framework for high-dimensional matrix time series that bridges the structural gap between matrix autoregression and matrix factor models.
Formulated the Matrix Autoregressive model with Common Factors (MARCF) by decomposing coefficient matrices into shared, predictor-specific, and response-specific components.
Developed a regularized gradient descent estimator to optimize over high-dimensional non-convex parameter spaces.
Evaluated theoretical convergence properties and tested empirical performance using synthetic simulations and global macroeconomic forecasting data.
Theoretical analysis established local linear convergence of the optimization algorithm and guaranteed statistical consistency under high-dimensional scaling.
Numerical simulations and empirical macroeconomic evaluations demonstrated superior estimation efficiency and model interpretability compared to standard benchmarks.