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

Yonghui Liu, Ruochen Sang, Shuangzhe Liu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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
Issue number2
Publication statusPublished - 1 May 2017

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