Bayesian structure selection for vector autoregression model

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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)422-439
Number of pages18
JournalJournal of Forecasting
Volume38
Issue number5
DOIs
Publication statusPublished - 2019 Aug 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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