TY - JOUR
T1 - A score test for detecting extreme values in a vector autoregressive model
AU - Liu, Yonghui
AU - Wang, Jing
AU - Shi, Dawei
AU - Leiva, Víctor
AU - Liu, Shuangzhe
N1 - Funding Information:
We would like to thank the Editors and Reviewers very much for their insightful and constructive comments which led to an improved presentation of the manuscript. The research of Y. Liu was supported by the National Social Science Fund of China [grant No. 19BTJ036]. The research of V. Leiva was partially funded by the National Agency for Research and Development (ANID) [project grant number FONDECYT 1200525] of the Chilean government under the Ministry of Science, Technology, Knowledge, and Innovation.
Publisher Copyright:
© 2023 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023
Y1 - 2023
N2 - In this paper, we propose a score test to study a vector autoregressive model and its detection of extreme values. We take a likelihood approach to derive the corresponding maximum likelihood estimators and information matrix. We establish the score statistic for the vector autoregressive model under two perturbation schemes for identifying possible influential cases or outliers. The effectiveness of the proposed diagnostics is examined by a simulation study. To make an application, a data analysis is performed using the model to fit monthly log-returns of International Business Machines Corporation stock and the Standard & Poor's 500 index. Lastly, comparisons between the results by the score test and the local influence method are made. We establish two important findings that the score test is more effective while the local influence analysis can be used to diagnose more influential cases.
AB - In this paper, we propose a score test to study a vector autoregressive model and its detection of extreme values. We take a likelihood approach to derive the corresponding maximum likelihood estimators and information matrix. We establish the score statistic for the vector autoregressive model under two perturbation schemes for identifying possible influential cases or outliers. The effectiveness of the proposed diagnostics is examined by a simulation study. To make an application, a data analysis is performed using the model to fit monthly log-returns of International Business Machines Corporation stock and the Standard & Poor's 500 index. Lastly, comparisons between the results by the score test and the local influence method are made. We establish two important findings that the score test is more effective while the local influence analysis can be used to diagnose more influential cases.
KW - case-weight perturbation model
KW - local influence
KW - mean-shift perturbation model
KW - Monte Carlo simulation
KW - multivariate normal distribution
KW - score statistic
KW - vector autoregressive model
UR - http://www.scopus.com/inward/record.url?scp=85158103216&partnerID=8YFLogxK
U2 - 10.1080/00949655.2023.2205647
DO - 10.1080/00949655.2023.2205647
M3 - Article
AN - SCOPUS:85158103216
SN - 0094-9655
VL - 93
SP - 2751
EP - 2779
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
IS - 15
ER -