Robust autoregressive modeling and its diagnostic analytics with a COVID-19 related application

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dc.contributor.authorLiu, Yonghui
dc.contributor.authorWang, Jing
dc.contributor.authorLeiva, Victor
dc.contributor.authorTapia, Alejandra
dc.contributor.authorTan, Wei
dc.contributor.authorLiu, Shuangzhe
dc.date.accessioned2025-01-20T17:12:08Z
dc.date.available2025-01-20T17:12:08Z
dc.date.issued2024
dc.description.abstractAutoregressive models in time series are useful in various areas. In this article, we propose a skew-t autoregressive model. We estimate its parameters using the expectation-maximization (EM) method and develop the influence methodology based on local perturbations for its validation. We obtain the normal curvatures for four perturbation strategies to identify influential observations, and then to assess their performance through Monte Carlo simulations. An example of financial data analysis is presented to study daily log-returns for Brent crude futures and investigate possible impact by the COVID-19 pandemic.
dc.fuente.origenWOS
dc.identifier.doi10.1080/02664763.2023.2198178
dc.identifier.eissn1360-0532
dc.identifier.issn0266-4763
dc.identifier.urihttps://doi.org/10.1080/02664763.2023.2198178
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91233
dc.identifier.wosidWOS:000970873800001
dc.issue.numero7
dc.language.isoen
dc.pagina.final1343
dc.pagina.inicio1318
dc.revistaJournal of applied statistics
dc.rightsacceso restringido
dc.subjectEM algorithm
dc.subjectinfluence diagnostics
dc.subjectmatrix differential calculus
dc.subjectMonte Carlo simulations
dc.subjectskew-t innovation
dc.subjecttime series models
dc.titleRobust autoregressive modeling and its diagnostic analytics with a COVID-19 related application
dc.typeartículo
dc.volumen51
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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