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Bayesian dynamic graphical models for high-dimensional vector autoregressions with time-varying parameters and volatility discounting

Seyma Vahap

Econometrics and Statistics2025https://doi.org/10.1016/j.ecosta.2025.07.001article
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https://doi.org/https://doi.org/10.1016/j.ecosta.2025.07.001

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@article{seyma2025,
  title        = {{Bayesian dynamic graphical models for high-dimensional vector autoregressions with time-varying parameters and volatility discounting}},
  author       = {Seyma Vahap},
  journal      = {Econometrics and Statistics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ecosta.2025.07.001},
}

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Bayesian dynamic graphical models for high-dimensional vector autoregressions with time-varying parameters and volatility discounting

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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