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Diagnostic analysis for a vector autoregressive model under Student ′ s t ‐distributions
Author(s) -
Liu Yonghui,
Sang Ruochen,
Liu Shuangzhe
Publication year - 2017
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/stan.12102
Subject(s) - autoregressive model , estimator , star model , mathematics , statistics , curvature , econometrics , autoregressive integrated moving average , time series , geometry
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.