Sparse additive matrix autoregressive model†
Jinhyeok Kim1 · Changryong Baek2
12Department of Statistics, Sungkyunkwan University
Correspondence to: † This work was supported by the Basic Science Research Program from the National Research Foundation of Korea (NRF-2022R1F1A1066209).
1 Graduate student, Department of Statistics, Sungkyunkwan University, Seoul 03063, Korea.
2 Corresponding author: Professor, Department of Statistics, Sungkyunkwan University, Seoul 03063, Korea. E-mail: crbaek@skku.edu
1 Graduate student, Department of Statistics, Sungkyunkwan University, Seoul 03063, Korea.
2 Corresponding author: Professor, Department of Statistics, Sungkyunkwan University, Seoul 03063, Korea. E-mail: crbaek@skku.edu
Received September 5, 2024; Revised October 2, 2024; Accepted October 13, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- This paper introduces the sparse additive matrix autoregressive (SAdd-MAR) model for analyzing high-dimensional matrix-valued time series data. The model extends MAR model by incorporating L1 regularization for sparsity. It also incorporates cross-sectional dependencies in the matrix-valued time series. We also adapt estimation method of Zhang (2024) by incorporating sparse estimation and thresholding to enhance model accuracy and reduce computational cost. Extensive Monte Carlo simulations show that our proposed method performs well. We also apply our model to economic indicators from OECD countries, demonstrating its superior forecasting power compared to other models.
- Keywords : Adaptive lasso, Add-MAR, high-dimensional time series, Lasso, threshold estimator
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