High-dimensional and banded vector autoregressions

Guo, Shaojun, Wang, Yazhen and Yao, Qiwei ORCID: 0000-0003-2065-8486 (2016) High-dimensional and banded vector autoregressions. Biometrika, 103 (4). pp. 889-903. ISSN 0006-3444

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Abstract

We consider a class of vector autoregressive models with banded coefficient matrices. The setting represents a type of sparse structure for high-dimensional time series, though the implied autocovariance matrices are not banded. The structure is also practically meaningful when the order of component time series is arranged appropriately. The convergence rates for the estimated banded autoregressive coefficient matrices are established. We also propose a Bayesian information criterion for determining the width of the bands in the coefficient matrices, which is proved to be consistent. By exploring some approximate banded structure for the autocovariance functions of banded vector autoregressive processes, consistent estimators for the auto-covariance matrices are constructed.

Item Type:	Article
Official URL:	http://biomet.oxfordjournals.org/
Additional Information:	© 2016 The Authors
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics
Date Deposited:	07 Sep 2016 10:38
Last Modified:	12 Dec 2024 01:14
URI:	http://eprints.lse.ac.uk/id/eprint/67606

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