Geodesic
Change point detection in vector autoregression
Kybernetika, Tome 54 (2018) no. 6, pp. 1122-1137

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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.
DOI : 10.14736/kyb-2018-6-1122
Classification : 62E20, 62M10
Keywords: vector autoregression; change point; quasi-maximum likelihood 
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     title = {Change point detection in vector autoregression},
     journal = {Kybernetika},
     pages = {1122--1137},
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     volume = {54},
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     zbl = {07031764},
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Prášková, Zuzana. Change point detection in vector autoregression. Kybernetika, Tome 54 (2018) no. 6, pp. 1122-1137. doi: 10.14736/kyb-2018-6-1122

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