Robust test for structural instability in dynamic factor models

In this paper, we consider a robust test for structural breaks in dynamic factor models. The proposed framework considers structural changes when the underlying high-dimensional time series is contaminated by outlying observations, which are often observed in many real applications such as fMRI, eco...

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Bibliographic Details
Published inAnnals of the Institute of Statistical Mathematics Vol. 73; no. 4; pp. 821 - 853
Main Authors Kim, Byungsoo, Song, Junmo, Baek, Changryong
Format Journal Article
LanguageEnglish
Published Tokyo Springer Japan 01.08.2021
Springer Nature B.V
Subjects
Autoregressive models
Dimensional changes
Divergence
Dynamic stability
Economics
Finance
Insurance
Management
Mathematics
Mathematics and Statistics
Robustness
Statistics
Statistics for Business
Structural stability
Outliers
Parameter change test
Dynamic factor models
High-dimensional time series
Minimum density power divergence
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Summary:In this paper, we consider a robust test for structural breaks in dynamic factor models. The proposed framework considers structural changes when the underlying high-dimensional time series is contaminated by outlying observations, which are often observed in many real applications such as fMRI, economics and finance. We propose a test based on the robust estimation of a vector autoregressive model for principal component factors using minimum density power divergence. The simulations study shows excellent finite sample performance, higher powers while achieving good sizes in all cases considered. Our method is illustrated to the resting state fMRI series to detect brain connectivity changes.
ISSN:0020-3157
1572-9052
DOI:10.1007/s10463-020-00773-0