@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",
note = "Funding Information: We are very grateful to the reviewers and the Editor for their constructive comments, which led to an improved version of the manuscript. We are also thankful to Professors A.C. Atkinson, R.D. Cook and D. von Rosen for their useful suggestions, when the paper was presented at the International Conference on Trends and Perspectives in Linear Statistical Inference held at Link{\"o}ping University, Link{\"o}ping, Sweden. The research of the first author is supported by the Natural Science Foundation of China (11271259). Publisher Copyright: {\textcopyright} 2016 The Authors. Statistica Neerlandica {\textcopyright} 2016 VVS.",
year = "2017",
month = may,
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",
}