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Title: Change point detection in vector autoregression (English)
Author: Prášková, Zuzana
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 54
Issue: 6
Year: 2018
Pages: 1122-1137
Summary lang: English
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Category: math
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Summary: In the paper a sequential monitoring scheme is proposed to detect instability of parameters in a multivariate autoregressive process. The proposed monitoring procedure is based on the quasi-likelihood scores and the quasi-maximum likelihood estimators of the respective parameters computed from a training sample, and it is designed so that the sequential test has a small probability of a false alarm and asymptotic power one as the size of the training sample is sufficiently large. The asymptotic distribution of the detector statistic is established under both the null hypothesis of no change as well as under the alternative that a change occurs. (English)
Keyword: vector autoregression
Keyword: change point
Keyword: quasi-maximum likelihood
MSC: 62E20
MSC: 62M10
idZBL: Zbl 07031764
idMR: MR3902624
DOI: 10.14736/kyb-2018-6-1122
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Date available: 2019-02-18T14:42:05Z
Last updated: 2020-01-05
Stable URL: http://hdl.handle.net/10338.dmlcz/147600
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