Change point detection in vector autoregression

Zuzana Prášková

Kybernetika (2018)

  • Volume: 54, Issue: 6, page 1122-1137
  • ISSN: 0023-5954

Abstract

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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.

How to cite

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Prášková, Zuzana. "Change point detection in vector autoregression." Kybernetika 54.6 (2018): 1122-1137. <http://eudml.org/doc/294752>.

@article{Prášková2018,
abstract = {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.},
author = {Prášková, Zuzana},
journal = {Kybernetika},
keywords = {vector autoregression; change point; quasi-maximum likelihood},
language = {eng},
number = {6},
pages = {1122-1137},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Change point detection in vector autoregression},
url = {http://eudml.org/doc/294752},
volume = {54},
year = {2018},
}

TY - JOUR
AU - Prášková, Zuzana
TI - Change point detection in vector autoregression
JO - Kybernetika
PY - 2018
PB - Institute of Information Theory and Automation AS CR
VL - 54
IS - 6
SP - 1122
EP - 1137
AB - 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.
LA - eng
KW - vector autoregression; change point; quasi-maximum likelihood
UR - http://eudml.org/doc/294752
ER -

References

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