Bayesian Dynamic Tensor Regression
Bibliographic Data
| ID | 19420180 |
|---|---|
| Authors | Monica Billio (0000-0002-6690-0832, Ca' Foscari University of Venice), Roberto Casarin (0000-0003-1746-9190, Ca' Foscari University of Venice), Matteo Iacopini (0000-0002-3551-4891, Vrije Universiteit Amsterdam, corresponding author), Sylvia Kaufmann (0000-0003-1918-3321, Study Center Gerzensee, Foundation of the Swiss National Bank, Gerzensee, Switzerland) |
| Year | 2023 |
| Volume | 41 |
| Issue | 2 |
| Pages | 429-439 |
| Publication date | 2023-04-03 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2022.2032721 |
| OpenAlex | W4210650204 |
| Language | EN |
| Citations received | 2 |
| References cited | 45 |
High-and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors and call for appropriate statistical tools. We propose a new linear autoregressive tensor process (ART) for tensor-valued data, that encompasses some well-known time series models as special cases. We study its properties and derive the associated impulse response function. We exploit the PARAFAC low-rank decomposition for providing a parsimonious parameterization and develop a Bayesian inference allowing for shrinking effects. We apply the ART model to time series of multilayer networks and study the propagation of shocks across nodes, layers and time
Algorithm · Autoregressive model · Bayesian inference · Bayesian probability · Econometrics · Impulse response · Inference · Advanced Neuroimaging Techniques and Applications · Computer Science · Gaussian Processes and Bayesian Inference · Mathematics · Tensor decomposition and applications · Artificial Intelligence
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |