The EAS approach for graphical selection consistency in vector autoregression models-Reference-Cited by-同舟云学术

The EAS approach for graphical selection consistency in vector autoregression models

Published:2022-09-27 Issue:2 Volume:51 Page:674-703
ISSN:0319-5724
Container-title:Canadian Journal of Statistics
language:en
Short-container-title:Can J Statistics

Author:

Williams Jonathan P.¹^ORCID,Xie Yuying²,Hannig Jan³

Affiliation:

1. Department of Statistics, North Carolina State University Raleigh North Carolina USA

2. Department of Statistics and Probability, Department of Computational Mathematics, Science and Engineering, Michigan State University East Lansing Michigan USA

3. Department of Statistics and Operations Research, University of North Carolina Chapel Hill North Carolina USA

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjs.11726

Reference51 articles.

1. Sparse graphical vector autoregression: A Bayesian approach;Ahelegbey D. F.;Annals of Economics and Statistics/Annales d'Économie et de Statistique,2016

2. The pseudo-marginal approach for efficient Monte Carlo computations

3. Large Bayesian vector auto regressions

4. Low Rank and Structured Modeling of High-Dimensional Vector Autoregressions

5. Regularized estimation in sparse high-dimensional time series models

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