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Discussion of “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions”

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Abstract

The author focuses on the “decoupling and recoupling” idea that can critically increase both computational and forecasting efficiencies in practical problems for economic and financial data. My discussion is twofold. First, I briefly describe the idea with an example of time-varying vector autoregressions, which are widely used in the context. Second, I highlight the issue of how to assess patterns of simultaneous relationships.

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Author information

Correspondence to Jouchi Nakajima.

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The Related Articles are https://doi.org/10.1007/s10463-019-00741-3; https://doi.org/10.1007/s10463-019-00743-1; https://doi.org/10.1007/s10463-019-00744-0.

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Nakajima, J. Discussion of “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions”. Ann Inst Stat Math 72, 33–36 (2020) doi:10.1007/s10463-019-00742-2

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Keywords

  • Bayesian forecasting
  • Decouple/recouple
  • Time-varying vector autoregressions
  • Multivariate time-series models