Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages-Reference-Cited by-同舟云学术

Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages

Published:2021-08-09 Issue: Volume: Page:1-12
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Wilms Ines¹,Basu Sumanta²,Bien Jacob³,Matteson David S.²

Affiliation:

1. Department of Quantitative Economics, Maastricht University, Maastricht, The Netherlands

2. Department of Statistics and Data Science, Cornell University, Ithaca, NY

3. Data Sciences and Operations, University of Southern California, Los Angeles, CA

Funder

National Institutes of Health

National Science Foundation

European Union

NSF

NIH

NSF CAREER

Xerox PARC

Cornell University Atkinson Center for a Sustainable Future

USAID

Cornell University Institute of Biotechnology & NYSTAR

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2021.1942013

Reference54 articles.

1. Agarwal, A., Negahban, S., and Wainwright, M. J. (2010), “Fast Global Convergence Rates of Gradient Methods for High-Dimensional Statistical Recovery,” in Advances in Neural Information Processing Systems, eds. J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel and A. Culotta, Curran Associates, Inc. pp. 3745. https://proceedings.neurips.cc/paper/2010/file/7cce53cf90577442771720a370c3c723-Paper.pdf

2. A new look at the statistical model identification

3. Canonical Correlation Analysis of Time Series and the Use of an Information Criterion

4. VARMA versus VAR for Macroeconomic Forecasting

5. Two Canonical VARMA Forms: Scalar Component Models Vis-à-Vis the Echelon Form

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