Diagnostic Analytics for an Autoregressive Model under the Skew-Normal Distribution

Yonghui Liu, Guohua Mao, Víctor Leiva, Shuangzhe Liu and Alejandra Tapia
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Yonghui Liu: School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China
Guohua Mao: School of Mathematics, Shanghai University of Finance and Economics, Shanghai 200433, China
Víctor Leiva: School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
Shuangzhe Liu: Faculty of Science and Technology, University of Canberra, Bruce, ACT 2617, Australia
Alejandra Tapia: School of Engineering in Statistics, Universidad Católica del Maule, Talca 3466706, Chile

Mathematics, 2020, vol. 8, issue 5, 1-19

Abstract: Autoregressive models have played an important role in time series. In this paper, an autoregressive model based on the skew-normal distribution is considered. The estimation of its parameters is carried out by using the expectation–maximization algorithm, whereas the diagnostic analytics are conducted by means of the local influence method. Normal curvatures for the model under four perturbation schemes are established. Simulation studies are conducted to evaluate the performance of the proposed procedure. In addition, an empirical example involving weekly financial return data are analyzed using the procedure with the proposed diagnostic analytics, which has improved the model fit.

Keywords: AR models; EM algorithm; local influence method; maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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