Bayesian structure selection for vector autoregression model

Chi Hsiang Chu, Mong Na Lo Huang, Shih Feng Huang, Ray-Bing Chen

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


A vector autoregression (VAR) model is powerful for analyzing economic data as it can be used to simultaneously handle multiple time series from different sources. However, in the VAR model, we need to address the problem of substantial coefficient dimensionality, which would cause some computational problems for coefficient inference. To reduce the dimensionality, one could take model structures into account based on prior knowledge. In this paper, group structures of the coefficient matrices are considered. Because of the different types of VAR structures, corresponding Markov chain Monte Carlo algorithms are proposed to generate posterior samples for performing inference of the structure selection. Simulation studies and a real example are used to demonstrate the performances of the proposed Bayesian approaches.

頁(從 - 到)422-439
期刊Journal of Forecasting
出版狀態Published - 2019 八月 1

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

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