Skew generalized normal innovations for the AR(p) process endorsing asymmetry

Show simple item record Neethling, Ane Ferreira, Johannes Theodorus Bekker, Andriette, 1958- Naderi, Mehrdad 2021-03-02T08:10:53Z 2021-03-02T08:10:53Z 2020-07-29
dc.description.abstract The assumption of symmetry is often incorrect in real-life statistical modeling due to asymmetric behavior in the data. This implies a departure from the well-known assumption of normality defined for innovations in time series processes. In this paper, the autoregressive (AR) process of order p (i.e., the AR(p) process) is of particular interest using the skew generalized normal (SGN) distribution for the innovations, referred to hereafter as the ARSGN(p) process, to accommodate asymmetric behavior. This behavior presents itself by investigating some properties of the SGN distribution, which is a fundamental element for AR modeling of real data that exhibits non-normal behavior. Simulation studies illustrate the asymmetry and statistical properties of the conditional maximum likelihood (ML) parameters for the ARSGN(p) model. It is concluded that the ARSGN(p) model accounts well for time series processes exhibiting asymmetry, kurtosis, and heavy tails. Real time series datasets are analyzed, and the results of the ARSGN(p) model are compared to previously proposed models. The findings here state the effectiveness and viability of relaxing the normal assumption and the value added for considering the candidacy of the SGN for AR time series processes. en_ZA
dc.description.department Statistics en_ZA
dc.description.librarian am2021 en_ZA
dc.description.sponsorship The National Research Foundation, South Africa, the South African NRF SARChI Research Chair in Computational and Methodological Statistics, the South African DST-NRF-MRC SARChI Research Chair in Biostatistics and the Research Development Programme at UP. en_ZA
dc.description.uri en_ZA
dc.identifier.citation Neethling, A., Ferreira, J., Bekker, A. et al. 2020, 'Skew generalized normal innovations for the AR(p) process endorsing asymmetry', Symmetry, vol. 12, art. 1253, pp. 1-23. en_ZA
dc.identifier.issn 2073-8994 (online)
dc.identifier.other 10.3390/sym12081253
dc.language.iso en en_ZA
dc.publisher MDPI Publishing en_ZA
dc.rights © 2020 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. en_ZA
dc.subject Conditional maximum likelihood estimator en_ZA
dc.subject Skew-t en_ZA
dc.subject Generalized normal en_ZA
dc.subject Heavy tails en_ZA
dc.subject Skewness en_ZA
dc.subject Skew generalized normal (SGN) en_ZA
dc.title Skew generalized normal innovations for the AR(p) process endorsing asymmetry en_ZA
dc.type Article en_ZA

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