Diagnostic analytics for an autoregressive model under the skew-normal distribution

Yonghui Liu, Guohua Mao, VICTOR ELISEO LEIVA SANCHEZ, Shuangzhe Liu, Alejandra Tapia

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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.

Original languageEnglish
Article number693
JournalMathematics
Volume8
Issue number5
DOIs
StatePublished - 1 May 2020
Externally publishedYes

Keywords

  • AR models
  • EM algorithm
  • Local influence method
  • Maximum likelihood estimation

Fingerprint Dive into the research topics of 'Diagnostic analytics for an autoregressive model under the skew-normal distribution'. Together they form a unique fingerprint.

Cite this