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A flexible two-piece normal dynamic linear model

Author

Listed:
  • Emanuele Aliverti

    (Università di Padova)

  • Reinaldo B. Arellano-Valle

    (Pontificia Universidad Católica de Chile)

  • Fereshteh Kahrari

    (Università di Padova)

  • Bruno Scarpa

    (Università di Padova
    Università di Padova)

Abstract

We construct a flexible dynamic linear model for the analysis and prediction of multivariate time series, assuming a two-piece normal initial distribution for the state vector. We derive a novel Kalman filter for this model, obtaining a two components mixture as predictive and filtering distributions. In order to estimate the covariance of the error sequences, we develop a Gibbs-sampling algorithm to perform Bayesian inference. The proposed approach is validated and compared with a Gaussian dynamic linear model in simulations and on a real data set.

Suggested Citation

  • Emanuele Aliverti & Reinaldo B. Arellano-Valle & Fereshteh Kahrari & Bruno Scarpa, 2023. "A flexible two-piece normal dynamic linear model," Computational Statistics, Springer, vol. 38(4), pages 2075-2096, December.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:4:d:10.1007_s00180-023-01355-3
    DOI: 10.1007/s00180-023-01355-3
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