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Bayesian structure selection for vector autoregression model

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  • Chi‐Hsiang Chu
  • Mong‐Na Lo Huang
  • Shih‐Feng Huang
  • Ray‐Bing Chen

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.

Suggested Citation

  • Chi‐Hsiang Chu & Mong‐Na Lo Huang & Shih‐Feng Huang & Ray‐Bing Chen, 2019. "Bayesian structure selection for vector autoregression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(5), pages 422-439, August.
  • Handle: RePEc:wly:jforec:v:38:y:2019:i:5:p:422-439
    DOI: 10.1002/for.2573
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    Cited by:

    1. Lai, Wei-Ting & Chen, Ray-Bing & Chen, Ying & Koch, Thorsten, 2022. "Variational Bayesian inference for network autoregression models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    2. Shih-Feng Huang & Hsin-Han Chiang & Yu-Jun Lin, 2021. "A network autoregressive model with GARCH effects and its applications," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-18, July.

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