Abstract
A Bayesian method is proposed for variable selection in high-dimensional matrix autoregressive models which reflects and exploits the original matrix structure of data to (a) reduce dimensionality and (b) foster interpretability of multidimensional relationship structures. A compact form of the model is derived which facilitates the estimation procedure and two computational methods for the estimation are proposed: a Markov chain Monte Carlo algorithm and a scalable Bayesian EM algorithm. Being based on the spike-and-slab framework for fast posterior mode identification, the latter enables Bayesian data analysis of matrix-valued time series at large scales. The theoretical properties, comparative performance, and computational efficiency of the proposed model is investigated through simulated examples and an application to a panel of country economic indicators.
Original language | English (US) |
---|---|
Article number | 91 |
Journal | Statistics and Computing |
Volume | 34 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2024 |
Bibliographical note
Publisher Copyright:© The Author(s) 2024.
Keywords
- Autoregressive models
- Bayesian estimation
- Matrix-valued time series
- Maximum a posteriori probability
- Stochastic search