Diagnostic analysis for a vector autoregressive model under Students t-distributions

Yonghui Liu, Ruochen Sang, Shuangzhe Liu

Research output: Contribution to journalArticle

Abstract

In this paper, we use the local influence method to study a vector autoregressive model under Students t-distributions. We present the maximum likelihood estimators and the information matrix. We establish the normal curvature diagnostics for the vector autoregressive model under three usual perturbation schemes for identifying possible influential observations. The effectiveness of the proposed diagnostics is examined by a simulation study, followed by our data analysis using the model to fit the weekly log returns of Chevron stock and the Standard & Poor's 500 Index as an application.

Original languageEnglish
Pages (from-to)86-114
Number of pages29
JournalStatistica Neerlandica
Volume71
Issue number2
Early online date23 Nov 2016
DOIs
Publication statusPublished - 1 May 2017

Fingerprint

Vector Autoregressive Model
t-distribution
Diagnostics
Normal Curvature
Influential Observations
Local Influence
Information Matrix
Maximum Likelihood Estimator
Data analysis
Simulation Study
Perturbation
Vector autoregressive model
Student-t distribution
Model
Standards
Curvature
Simulation study
Maximum likelihood estimator

Cite this

@article{29a2f0f944d14696becbe998566898a1,
title = "Diagnostic analysis for a vector autoregressive model under Student′s t-distributions",
abstract = "In this paper, we use the local influence method to study a vector autoregressive model under Student′s t-distributions. We present the maximum likelihood estimators and the information matrix. We establish the normal curvature diagnostics for the vector autoregressive model under three usual perturbation schemes for identifying possible influential observations. The effectiveness of the proposed diagnostics is examined by a simulation study, followed by our data analysis using the model to fit the weekly log returns of Chevron stock and the Standard & Poor's 500 Index as an application.",
keywords = "curvature, maximum likelihood estimator, perturbation scheme, Student's t-distribution, vector autoregressive model",
author = "Yonghui Liu and Ruochen Sang and Shuangzhe Liu",
year = "2017",
month = "5",
day = "1",
doi = "10.1111/stan.12102",
language = "English",
volume = "71",
pages = "86--114",
journal = "Statistica Neerlandica",
issn = "0039-0402",
publisher = "Wiley-Blackwell",
number = "2",

}

Diagnostic analysis for a vector autoregressive model under Students t-distributions. / Liu, Yonghui; Sang, Ruochen; Liu, Shuangzhe.

In: Statistica Neerlandica, Vol. 71, No. 2, 01.05.2017, p. 86-114.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Diagnostic analysis for a vector autoregressive model under Student′s t-distributions

AU - Liu, Yonghui

AU - Sang, Ruochen

AU - Liu, Shuangzhe

PY - 2017/5/1

Y1 - 2017/5/1

N2 - In this paper, we use the local influence method to study a vector autoregressive model under Student′s t-distributions. We present the maximum likelihood estimators and the information matrix. We establish the normal curvature diagnostics for the vector autoregressive model under three usual perturbation schemes for identifying possible influential observations. The effectiveness of the proposed diagnostics is examined by a simulation study, followed by our data analysis using the model to fit the weekly log returns of Chevron stock and the Standard & Poor's 500 Index as an application.

AB - In this paper, we use the local influence method to study a vector autoregressive model under Student′s t-distributions. We present the maximum likelihood estimators and the information matrix. We establish the normal curvature diagnostics for the vector autoregressive model under three usual perturbation schemes for identifying possible influential observations. The effectiveness of the proposed diagnostics is examined by a simulation study, followed by our data analysis using the model to fit the weekly log returns of Chevron stock and the Standard & Poor's 500 Index as an application.

KW - curvature

KW - maximum likelihood estimator

KW - perturbation scheme

KW - Student's t-distribution

KW - vector autoregressive model

UR - http://www.scopus.com/inward/record.url?scp=85006000172&partnerID=8YFLogxK

U2 - 10.1111/stan.12102

DO - 10.1111/stan.12102

M3 - Article

VL - 71

SP - 86

EP - 114

JO - Statistica Neerlandica

JF - Statistica Neerlandica

SN - 0039-0402

IS - 2

ER -