Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models
Author:
Affiliation:
1. Department of Statistics, University of Florida, Gainesville, FL
2. Computer and Information Science Engineering, University of Florida, Gainesville, FL
3. Informatics Institute, University of Florida, Gainesville, FL
Funder
NSF
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2022.2079514
Reference41 articles.
1. Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
2. Nested Reduced-Rank Autoregressive Models for Multiple Time Series
3. Large Dimensional Factor Analysis
4. Multiple Change Points Detection in Low Rank and Sparse High Dimensional Vector Autoregressive Models
5. Large Bayesian vector auto regressions
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1. High-Dimensional Time Series Segmentation via Factor-Adjusted Vector Autoregressive Modeling;Journal of the American Statistical Association;2023-07-26
2. VAR, ARIMAX and ARIMA models for nowcasting unemployment rate in Ghana using Google trends;Journal of Electrical Systems and Information Technology;2023-02-27
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