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This study introduces Bayesian dynamic factor models for analyzing mixed-time series data. The new framework and algorithms effectively model intertwined financial risks, highlighting the importance of joint analysis.

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Area of Science:

  • Statistics
  • Econometrics
  • Computational Statistics

Background:

  • Traditional time series models often struggle with mixed-measurement data.
  • Jointly modeling diverse data types is crucial for accurate risk assessment.

Purpose of the Study:

  • To propose generalized Bayesian dynamic factor models for jointly modeling mixed-measurement time series.
  • To develop efficient Bayesian computational algorithms for inference.

Main Methods:

  • Generalized Bayesian dynamic factor models.
  • Metropolis Hastings algorithm with adaptive proposals.
  • Greedy Density Kernel Approximation (GDKA) and parameter expansion.

Main Results:

  • The proposed framework effectively models mixed-scale measurements conditionally on latent factors.
  • Developed efficient algorithms for posterior inference on latent factors and parameters.
  • Demonstrated the importance of joint modeling in a financial risk analysis.

Conclusions:

  • The generalized Bayesian dynamic factor models provide a robust framework for mixed-time series.
  • The developed algorithms are efficient for complex Bayesian inference.
  • Jointly modeling mixed-measurement time series is essential for understanding intertwined risks.