Additive autoregressive models for matrix valued time series

In this article, we develop additive autoregressive models (Add-ARM) for the time series data with matrix valued predictors. The proposed models assume separable row, column and lag effects of the matrix variables, attaining stronger interpretability when compared with existing bilinear matrix autoregressive models. We utilize the Gershgorin's circle theorem to impose some certain conditions on the parameter matrices, which make the underlying process strictly stationary. We also introduce the alternating least squares estimation method to solve the involved equality constrained optimization problems. Asymptotic distributions of the parameter estimators are derived. In addition, we employ hypothesis tests to run diagnostics on the parameter matrices. The performance of the proposed models and methods is further demonstrated through simulations and real data analysis.

Automated summary

In this article, the authors propose additive autoregressive models (Add-ARM) for time series data with matrix valued predictors. The models assume separable row, column and lag effects of the matrix variables, providing stronger interpretability compared to existing bilinear matrix autoregressive models. The authors utilize Gershgorin's circle theorem to impose specific conditions on the parameter matrices, ensuring strict stationarity of the underlying process. They also introduce the alternating least squares estimation method to solve the involved optimization problems. The performance of the models and methods is demonstrated through simulations and real data analysis.