TY - JOUR
T1 - Diagnostic analystics in the Bayesian vector autoregressive model
AU - Liu, Yonghui
AU - Yao, Zhao
AU - Wang, Qingrui
AU - Hao, Chengcheng
AU - Liu, Shuangzhe
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Funding Information:
The research conducted by Yonghui Liu was supported by the National Social Science Fund of China [Grant No. 22&ZD160]. We would like to thank the Reviewers and Editors for their constructive and insightful comments, which have significantly improved the presentation of our manuscript. We would also like to thank Xin Song and Conan Liu for their technical assistance at the beginning of the project.
Publisher Copyright:
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024
Y1 - 2024
N2 - The vector autoregressive model is extensively employed in macroeconomics, finance, and the natural sciences. However, it often encounters the issue of over-parametrization, resulting in undesirable behavior within the model. In this paper, we utilize the Bayesian vector autoregressive model as a tool to tackle this problem. Additionally, we employ the Bayesian local influence method to detect possible extreme observations under the model. We use posterior inference to estimate related parameters and construct Bayesian perturbation schemes for priors, variance, and data perturbations. Bayesian local influence is conducted based on three objective functions: Bayes factor, ϕ divergence, and posterior mean. To demonstrate the effectiveness of the diagnostics, we conduct numerical simulations. In the real data example, we use US inflation, unemployment, and interest rate data to verify this method's effectiveness.
AB - The vector autoregressive model is extensively employed in macroeconomics, finance, and the natural sciences. However, it often encounters the issue of over-parametrization, resulting in undesirable behavior within the model. In this paper, we utilize the Bayesian vector autoregressive model as a tool to tackle this problem. Additionally, we employ the Bayesian local influence method to detect possible extreme observations under the model. We use posterior inference to estimate related parameters and construct Bayesian perturbation schemes for priors, variance, and data perturbations. Bayesian local influence is conducted based on three objective functions: Bayes factor, ϕ divergence, and posterior mean. To demonstrate the effectiveness of the diagnostics, we conduct numerical simulations. In the real data example, we use US inflation, unemployment, and interest rate data to verify this method's effectiveness.
KW - Bayesian local influence
KW - Bayesian perturbation schemes
KW - Bayesian vector autoregressive model
KW - multivariate normal distribution
UR - http://www.scopus.com/inward/record.url?scp=85203675640&partnerID=8YFLogxK
U2 - 10.1080/00949655.2024.2400524
DO - 10.1080/00949655.2024.2400524
M3 - Article
AN - SCOPUS:85203675640
SN - 0094-9655
VL - 94
SP - 3750
EP - 3766
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
IS - 17
ER -